2021-11-dowhy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Causal inference with DoWhy\n",
    "\n",
    "Jitao David Zhang, adapted from the [Quickstart tutorial of DoWhy](https://microsoft.github.io/dowhy/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html), November 2021."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## required libraries\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "import dowhy.datasets\n",
    "from IPython.display import Image, display\n",
    "import os\n",
    "\n",
    "## setting appropriate log/warning level\n",
    "import logging\n",
    "import warnings\n",
    "from sklearn.exceptions import DataConversionWarning\n",
    "logging.getLogger(\"dowhy\").setLevel(logging.WARNING)\n",
    "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "## for estimation with EconML\n",
    "import econml\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Z0        Z1        W0        W1        W2     v0         y\n",
      "0     1.0  0.837259 -0.395129 -1.799610 -1.351144   True -0.784172\n",
      "1     1.0  0.477086  0.382205 -2.994367 -0.274757   True -2.739608\n",
      "2     0.0  0.580271  0.355604 -2.017578 -1.038663   True -0.640631\n",
      "3     1.0  0.122054  0.804288 -0.596805  0.411247   True  7.007251\n",
      "4     0.0  0.140012  0.834496  0.791825 -1.441103   True  8.710545\n",
      "...   ...       ...       ...       ...       ...    ...       ...\n",
      "4995  0.0  0.082940 -0.404577 -1.101885 -0.412220  False -4.751625\n",
      "4996  0.0  0.970771 -0.757268 -2.900505  1.028887   True -0.751391\n",
      "4997  0.0  0.126145 -0.183822 -1.130745 -0.325476  False -4.585672\n",
      "4998  1.0  0.059718  0.310194 -0.981639  1.736881   True  7.665289\n",
      "4999  1.0  0.151852 -0.209015 -0.507616  0.009626   True  6.137256\n",
      "\n",
      "[5000 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "# Simulate data\n",
    "data = dowhy.datasets.linear_dataset(\n",
    "    beta=8, ## the true causal effect\n",
    "    num_common_causes=3,\n",
    "    num_instruments=2,\n",
    "    num_samples=5000,\n",
    "    treatment_is_binary=True)\n",
    "print(data[\"df\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Modelling\n",
    "\n",
    "The first step of causal inference is to encode our domain knowledge into a causal model, often represented as a network of nodes and edges. The network, known as graph, encodes variables and their dependencies.\n",
    "\n",
    "The outcome of causal inference critically depends on the input assumptions, so this step is important.\n",
    "\n",
    "1. *Confounders*: forks that affect both action and outcome\n",
    "2. *Instrumental variables*: start of the chain of IV-action-outcome, and not affected by any variable that affects the outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Model to find the causal effect of treatment ['v0'] on outcome ['y']\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A causal model from domain knowledge\n",
    "model = CausalModel(\n",
    "    data=data[\"df\"],\n",
    "    treatment=data[\"treatment_name\"],\n",
    "    outcome=data[\"outcome_name\"],\n",
    "    common_causes=data[\"common_causes_names\"],\n",
    "    instruments=data[\"instrument_names\"])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the graph\n",
    "model.view_model(layout=\"dot\")\n",
    "#display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph[directed 1node[ id \"y\" label \"y\"]node[ id \"Unobserved Confounders\" label \"Unobserved Confounders\"]edge[source \"Unobserved Confounders\" target \"y\"]node[ id \"W0\" label \"W0\"] node[ id \"W1\" label \"W1\"] node[ id \"W2\" label \"W2\"]node[ id \"Z0\" label \"Z0\"] node[ id \"Z1\" label \"Z1\"]node[ id \"v0\" label \"v0\"]edge[source \"Unobserved Confounders\" target \"v0\"]edge[source \"v0\" target \"y\"]edge[ source \"W0\" target \"v0\"] edge[ source \"W1\" target \"v0\"] edge[ source \"W2\" target \"v0\"]edge[ source \"Z0\" target \"v0\"] edge[ source \"Z1\" target \"v0\"]edge[ source \"W0\" target \"y\"] edge[ source \"W1\" target \"y\"] edge[ source \"W2\" target \"y\"]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## In the example baove, domain knowledge is specified with named variable sets of confounders and IVs\n",
    "## Alternatively, once and use a causal graph, like the example below shows\n",
    "print(data[\"gml_graph\"])\n",
    "model_by_graph = CausalModel(\n",
    "    data=data[\"df\"],\n",
    "    treatment=data[\"treatment_name\"][0],\n",
    "    outcome=data[\"outcome_name\"][0],\n",
    "    graph=data[\"gml_graph\"])\n",
    "model_by_graph.view_model(layout=\"dot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Identification\n",
    "\n",
    "Once both the underlying causal graph is setup and the question of interest is identified (for instance the effect of A on B) during the modelling step, we can proceed to the *identification* step. The goal of *identification* is to check whether the target quantity can be estimated given the observed variables, also known as the identifiability of the model. The check is performed based on the model alone: it has *no need* to access the data itself. In another word, once the model is setup, its identifiability can be checked before the data is observed.\n",
    "\n",
    "There are two main identification methods for causal inference, *the backdoor criterion* and *instrumental variable (IV) identification*.\n",
    "\n",
    "The backdoor criterion, more generally known as *adjustment sets*, makes the following assertion: if all common causes of the action A and the outcome Y are observed, the causal effect from A to Y can be identified by conditioning on all the common causes (i.e. the confounders).\n",
    "\n",
    "Here is an example of backdoor criterion. Let's assume that we want to estimate the effect of having a big lunch on Sunday on going for a walk in the afternoon. And let's assume that the weather is the only factor that affects both variables: good weather makes us attempt to eat much and to go for a walk, while bad weather decreases both the appetite and the interest of going outside. By measuring the food intake and the afternonon activity only, we may likely observe a observation, which is confounded by the weather. By observing the weather and investigating the relationship between food instake and afternoon activity conditional on the weather, we can better estimate the effect with the backdoor criterion.\n",
    "\n",
    "Besides the background criterion, if an instrumental variable is available, we can estimate the effect of the action A and the outocme Y even when confounders of action and outcome are unobserved. Following the definition of instrumental variable above, an instrument only affects the action directly, and is not affected by any variable that also affects the outcome. Therefore, the effect of the instrument on the outcome is achieve in two sequential steps: the effect of the instrument on the action, and the effect of the action on the treatment. By estimating the effect of instrument on the action, and the effect of instrument on the outcome, we can learn indirectly the effect of the action on the outcome. \n",
    "\n",
    "Instrumental variable is often considered as 'natural experiments', because it is not affected by any causes of the outcome, and exerts its effect on the outcome only through the action. An example in the genomics field is *cis*-eQTL (expression quantitative traits loci), which is a single-nucleotide polymorphism (SNP) that affects the expression of a nearby gene. Whether an individual has two copies of the major allele, one copy of the major allele and one copy of the minor allele, or two copies of minor alleles is up to the random process of chromosome separation in the meiosis and mating of sperms and eggs. Now, if the nearby gene affects a phenotype (say risk of developing a disease), and there is no other genetic or environmental factors that affects both the SNP and the phenotype, then we can use the observations of SNPs, expression of the nearby gene, and phenotypes in a population to estimate the causal effect of the gene on the phenotype, treating the SNP as an instrumental variable.\n",
    "\n",
    "In the case of binary instrument Z, the effect of action A on outcome Y is estimated by the ratio of the difference of outcome given the two versions of the instrument over the difference of action given the two versions of the instrument. This is because an instrument variable, such as in a natural experiment, causes changes in the outcome *via* the changes in the action. Therefore, the effect of action on the outcome is the amount of change in the outcome caused by the instrument divided by the amount of change in the action caused by the action.\n",
    "\n",
    "$$\n",
    "E[Y|do(A=1)] - E[Y|do(A=0)] = \\frac{E[Y|Z=1]-E[Y|Z=0]}{E[A|Z=1]-E[A|Z=0]}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                           \n",
      "─────(Expectation(y|W1,W0,W2))\n",
      "d[v₀]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,U) = P(y|v0,W1,W0,W2)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "Estimand expression:\n",
      "Expectation(Derivative(y, [Z1, Z0])*Derivative\n",
      "([v0], [Z1, Z0])**(-1))\n",
      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Identify causal effects and return target estimands\n",
    "## The estimation trinity: Father (Estimand), Son (Estimate), and Holy Spirit (Estimator)\n",
    "## Estimand (target of inference): a quantity that is to be estimated in a statistical analysis.\n",
    "## Related concepts:\n",
    "## Estimator (rule of inference): the rule for calculating an estimate of a given quantity based on observed data\n",
    "## Estimate (result of inference): the specific numeric value obtained from a given method and dataset\n",
    "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another estimand is using the frontdoor criterion and frontdoor adjustment. It will be explained in another tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimation step builds a statistical estimator that can compute the target estimand identified in the previous step. *DoWhy* implements a few standard etimators, while *EconML*, another package by Microsoft Research focusing on econometrics and economic decision making, implements more estimators that use machine learning.\n",
    "\n",
    "Below is an example of using Propensity Score Stratification using DoWhy. A propensity score is the probability of a unit being assigned to a treatment given a set of observed covariates. Using them, we can reduce selection bias by equating groups based on covariates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                           \n",
      "─────(Expectation(y|W1,W0,W2))\n",
      "d[v₀]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,U) = P(y|v0,W1,W0,W2)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W1+W0+W2\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 7.953591375840334\n",
      "95.0% confidence interval: (7.880159293948034, 8.101469965651821)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "propensity_strat_estimate = model.estimate_effect(identified_estimand,\n",
    "                                                  method_name=\"backdoor.dowhy.propensity_score_stratification\",\n",
    "                                                  confidence_intervals=True)\n",
    "print(propensity_strat_estimate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we show an alternative estimation procedure, using DoubleML. Double machine learning, or orthogonal machine learning, estimates treatment effects when all potential confoundators are observed. Confounders are known in the econometrics field also as *controls* (which can be confusing for a biologist or chemist, where a control means a sample without active treatment), i.e. factors that simutaneously affect the treatment decision and the observed outcome. Its power over classical statistical approach is that it can handle high-dimensional data, and it can model outcome non-parametrically.\n",
    "\n",
    "Double machine learning reduces the problem to first estimating *two predictive tasks*:\n",
    "\n",
    "1. Predicting the outcome from the confounders (*model_y* in the code),\n",
    "2. Predicting the treatment from the confounders (*model_t* in the code);\n",
    "\n",
    "Then th emodel combines the two predictive models in a final-stage estimation (*model_final* in the code), which creates a model of the heterogeneous treatment effect. Arbitrary machine learning methods can be used for the two predictive tasks, among others linear regression, reguaralized regression, kernel methods, non-parametric methods (for instance gradient boosting), random forrest (CausalForest)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                           \n",
      "─────(Expectation(y|W1,W0,W2))\n",
      "d[v₀]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,U) = P(y|v0,W1,W0,W2)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W1+W0+W2 | \n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 7.911127229532408\n",
      "Effect estimates: [7.91112723 7.91112723 7.91112723 ... 7.91112723 7.91112723 7.91112723]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dml_estimate = model.estimate_effect(identified_estimand,\n",
    "                                     method_name = \"backdoor.econml.dml.DML\",\n",
    "                                     method_params={\n",
    "                                        'init_params': {'model_y':GradientBoostingRegressor(),\n",
    "                                                        'model_t': GradientBoostingRegressor(),\n",
    "                                                        'model_final':LassoCV(fit_intercept=False), },\n",
    "                                        'fit_params': {}\n",
    "                                     },\n",
    "                                    confidence_intervals=False) ## LassoCV does not support stderr\n",
    "print(dml_estimate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also tried to use gradient boost regression as the final estimator, however the estimate is very different from the expected value. Is it because of the unparametric method compared with LassoCV? I am not sure yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                           \n",
      "─────(Expectation(y|W1,W0,W2))\n",
      "d[v₀]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,U) = P(y|v0,W1,W0,W2)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W1+W0+W2 | \n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 6.690513552085235\n",
      "Effect estimates: [6.69051355 6.69051355 6.69051355 ... 6.69051355 6.69051355 6.69051355]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The final model has a nonzero intercept for at least one outcome; it will be subtracted, but consider fitting a model without an intercept if possible.\n"
     ]
    }
   ],
   "source": [
    "dml_estimate_GBR = model.estimate_effect(identified_estimand,\n",
    "                                     method_name = \"backdoor.econml.dml.DML\",\n",
    "                                     method_params={\n",
    "                                        'init_params': {'model_y':GradientBoostingRegressor(),\n",
    "                                                        'model_t': GradientBoostingRegressor(),\n",
    "                                                        'model_final':GradientBoostingRegressor(), },\n",
    "                                        'fit_params': {}\n",
    "                                     },\n",
    "                                    confidence_intervals=False) ## LassoCV does not support stderr\n",
    "print(dml_estimate_GBR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Refutation\n",
    "\n",
    "The final step is to check the robustness of the estimate. Given that results of causal inference often cannot be directly validatd, it is critical to refutate the model predictions stringently. The goal is investigate the impacts of assumptions taken during the modelling, identification, and estimation steps on the results. \n",
    "\n",
    "Compared with machine learning, where robustness of a model is tested with a *test test*, retutation with *DoWhy* attempts to challenge the data with the model and the assumptions, i.e. to refute the correctness of an obtained estimate using properties of a good estimator. Take a simple example, the refutation test *placebo_treatment_refuter* checks whether the estimator returns an estimate of 0 when the action variable is replaced by a random variable, independent of all other variables. Thus, we  simulate *do* operations with random actions, and hope to get a zero average effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## CI refute not supported yet\n",
    "## refute_results = model.refute_estimate(identified_estimand,\n",
    "##                                       propensity_strat_estimate,\n",
    "##                                       method_name=\"placebo_treatment_refuter\",\n",
    "##                                       num_simulation=2)\n",
    "## print(refute_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                           \n",
      "─────(Expectation(y|W1,W0,W2))\n",
      "d[v₀]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W1,W0,W2,U) = P(y|v0,W1,W0,W2)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W1+W0+W2\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 7.953591375840334\n",
      "\n",
      "Refute: Use a Placebo Treatment\n",
      "Estimated effect:7.953591375840334\n",
      "New effect:0.00037333317040775984\n",
      "p value:0.47\n",
      "\n"
     ]
    }
   ],
   "source": [
    "propensity_strat_estimate_noCI = model.estimate_effect(identified_estimand,\n",
    "                                                  method_name=\"backdoor.dowhy.propensity_score_stratification\",\n",
    "                                                  confidence_intervals=False)\n",
    "print(propensity_strat_estimate_noCI)\n",
    "refute_results_noCI = model.refute_estimate(identified_estimand,\n",
    "                                       propensity_strat_estimate_noCI,\n",
    "                                       method_name=\"placebo_treatment_refuter\")\n",
    "print(refute_results_noCI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we use another technique, which simulates random confounders, to check their effect on the estimated effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add a Random Common Cause\n",
      "Estimated effect:7.953591375840334\n",
      "New effect:8.003659748217249\n",
      "\n"
     ]
    }
   ],
   "source": [
    "refute_results_randomCC = model.refute_estimate(identified_estimand,\n",
    "                                                propensity_strat_estimate,\n",
    "                                       method_name=\"random_common_cause\")\n",
    "print(refute_results_randomCC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using DoWhy and EconML: The Mystery Dataset problem\n",
    "\n",
    "Suppose we are given some data with treatment and outcome. Can we determine whether the treatment causes the outcome, or the correlation is purely due to another common cause?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Treatment    Outcome        w0         s\n",
      "0   6.218162  12.661710 -0.150403  8.962039\n",
      "1  18.710078  37.421932  3.558575  2.335602\n",
      "2  20.340590  40.785224 -3.783748  7.465029\n",
      "3  14.910764  29.674558 -2.984854  5.992456\n",
      "4  20.463757  40.770728  3.789475  3.458830\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import dowhy.datasets, dowhy.plotter\n",
    "\n",
    "rvar = 1 if np.random.uniform() > 0.2 else 0\n",
    "is_linear = False # A non-linear dataset. Change to True to see results for a linear dataset.\n",
    "data_dict = dowhy.datasets.xy_dataset(10000, \n",
    "                                      effect=rvar,\n",
    "                                      num_common_causes=1,\n",
    "                                      is_linear=is_linear,\n",
    "                                      sd_error=0.2)\n",
    "\n",
    "## nonlinear: a hard-coded wonder equation\n",
    "## V = 6 + W0*W0 + tterm + E1, ## V=treatment, W0=random hidden confounder\n",
    "## Y = 6 + W0*W0 + yterm + E2\n",
    "## if effect:\n",
    "##   Y += V #/20 # divide by 10 to scale the value of Y to be comparable to V\n",
    "## else:\n",
    "##   Y += (6 + W0)\n",
    "df = data_dict['df']\n",
    "print(df.head())\n",
    "dowhy.plotter.plot_treatment_outcome(df[data_dict[\"treatment_name\"]], df[data_dict[\"outcome_name\"]],\n",
    "                             df[data_dict[\"time_val\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mys_model= CausalModel(\n",
    "        data=df,\n",
    "        treatment=data_dict[\"treatment_name\"],\n",
    "        outcome=data_dict[\"outcome_name\"],\n",
    "        common_causes=data_dict[\"common_causes_names\"],\n",
    "        instruments=data_dict[\"instrument_names\"])\n",
    "mys_model.view_model(layout=\"dot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "     d                               \n",
      "────────────(Expectation(Outcome|w0))\n",
      "d[Treatment]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,U) = P(Outcome|Treatment,w0)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable found!\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mys_identified_estimand = mys_model.identify_effect(proceed_when_unidentifiable=True)\n",
    "print(mys_identified_estimand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "     d                               \n",
      "────────────(Expectation(Outcome|w0))\n",
      "d[Treatment]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,U) = P(Outcome|Treatment,w0)\n",
      "\n",
      "## Realized estimand\n",
      "b: Outcome~Treatment+w0\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 1.998957940667582\n",
      "\n",
      "Causal Estimate is 1.998957940667582\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnQAAAGTCAYAAAChwI9oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACSRklEQVR4nOzdd1yT1/4H8M9JSCDsqeBAqHsvrBsHKqIiCNhhW/V2WdveVju8an+3ave87bW1am+HtbW2lSkiLtx7a+qsCk6UIVM2Ob8/MprwPAkBwgh8369XXiHPOc/Jl/gIX85zBuOcgxBCCCGEWC9JYwdACCGEEELqhhI6QgghhBArRwkdIYQQQoiVo4SOEEIIIcTKUUJHCCGEEGLlKKEjhBBCCLFyNo0dACG15enpyf38/Bo7DEIIIaTO7t69C5VKBQAoKipCXl6eoI5CoUBRURETO58SOlIrjLFgALMADAXgDaAIwG0AhwHEcs63GDmvNYDXAUwF4Ks57xyANQDW8BosjOjn54fjx4/X4bsghBBCGo9SqURKSooueSsqKsLGjRuRnp4uqOvh4QG5XG60LUroSI0wxhwArAUQUaXIDoA7gN4AugAQJHSMsQAASQBa6R1WAAjUPB5hjIVzzkvrIXRCCCGkyVAqlUhISEBlZSUA4MaNG4iOjkZ+fr6gbr9+/TBp0iT8+OOPRtujhI6YjTEmB7AJwGgAlQBWA/gZwFUAUgDdAUQC8BE51xPARqiTuWwArwJIAeAC4J8AXgIwEcBXAJ6v3++EEEIIaTxKpRJxcXHgnEOlUmHfvn3YvXs3qt6kksvlCA0NRe/evattkxI6UhMLoU7mKgBEcM4Tq5TfBbDLxLk+AFQApnDOD+ud8zJjTAJgLoBnGWNfcc6Vlg6eEEIIaWxKpRKJiYngnCM/Px+xsbFIS0sT1GvTpg2ioqLg7u5uVruU0BGzMMY8ACzSvFwhksyZOlcG4FnNywS9ZE7fEk0dGYA5AF6uQ7iEEEJIk6S9zXr58mXEx8ejqKhIUGfo0KEICgqCjY35aRoldMRcT0E9To4D+E8Nzx0J9a1VAIgWq8A5z2SM7QEwDuoJE5TQEUIIsXr6Ex8YYygvL0dKSgoOHTokqGtvb49p06ahc+fOgrKIiAgsXbr0hLH3oYSOmGui5vkM5/yG9iBjzAZAZTWzUwfqfX3ERL0jUCd07RljnpzzrFpHSwghhDQy7e3V8vJyAEBWVhaio6NFZ7H6+/sjIiICTk5OBsddXFwQFBRU7Tg6SuiIubRJ2XHGmD2AfwF4EoAfAM4Y+wtAHIDPOefZVc7tonlWAbhu4j1S9b7uCoASOkIIIVYrOTlZl8ydOXMGSUlJKCsrM6jDGMPYsWMxfPhwSCSG+z0oFArMmzfPrPeihI5UizGmAOCpeVkE4BiAHlWqdYN6jN1MxlhIlUkN2nNzOOcVJt4qQ+9rjzqETAghhDQapVKJ5ORkFBcXo7S0FJs3b8aZM2cE9VxcXBAZGQlfX19BmVQqRUhIiNnvSQkdMYeL3tcvAJADiAGwDMAlAK0BPAPg3wDaAkhgjPXhnBdqznHQPJdU8z7Fel871jVoQgghpKElJSXpFr1PT09HdHQ0srOr3rgCunfvjqlTp0KhUAjKGGMICwsza7kSLUroiDmkel/LAewEMF1v3NxNAEsZYwUAPgPgD/VactrJE9ptSqrbBUJ/OxPRuoyx5zVti/5FQwghhDQWbTLHOcfRo0exbds23cLBWjY2NggODkZAQAAYE+7iJZPJzF57zqDdOkVOWorCKq/fNjIJ4iuob7t6AAjH3wmd9nzhnyGG7PS+fiBWgXP+LYBvASAgIMDsbcIIIYSQ+qJ/i7WoqAgJCQm4dOmSoJ6XlxeioqLQunVr0XbMnQAhhhI6Yo4CAKUAbKGe2CC6gSrnvIwxdgTAJBiOsdNObnBljNmYGEfnpfe1sH/aQjjnKCgoQH5+PoqKigR/PRFCSFMgkUhga2sLBwcHODs7w9bWtrFDIiKUSiXi4+OhUqmQlpaGmJgYFBQUCOoNGDAAEydOFOzHWpckTh8ldKRanHOVZhZrL6gnNpjaazVH8+ysd+yy5lkKwBfANSPn+ut9LfzTxgI458jIyMCDBw/g7u4Ob29vSKVS0W5vQghpLNotoUpKSlBYWIjr16/D29sbzs7O1Z9MGlRycjIqKiqwd+9e7NmzR7B9l62tLUJDQ9GrVy/BuQEBAZg8ebJF4qCEjpjrONQJnTtjzI5zbmyCg3Z2ap7eMf2FEAfDeEI3WPN8o77WoCsoKMCDBw/QoUMHSKXS6k8ghJBGwBiDVCqFg4MDHBwc4OLigps3b0Imk4kOoicNT7tg8N27dxEbG4vr14WrcrVt2xZRUVFwc3MTlEmlUoslcwAldMR8CQBmQz1xYQiA3VUrMMZs8XdSdlqvaB/UCZ4LgCgA60XO9QQwSvPS7G3Faio/Px/u7u6UzBFCrIqdnR3c3NyQk5NDCV0ToJ38cPHiRSQkJKC4uFhQZ/jw4Rg7dqzo7xupVIqwsDCLxiSpvgohAIDN+Hvh33cZY2LXzpsAtH+G/KY9yDkvB/Cd5mU4Y+xhkXOXQj2DlkMz6aE+FBUVwdGRVkQhhFgfFxcXFBZWnaNGGppSqcShQ4ewefNm/Pbbb4JkzsHBAU8++STGjx8vmsy5uLjUeEkSc1APHTGLZsLDfKh3gxgBYBNj7F0AF6Feh+5ZAK9pqp8AsLZKEx8BmAHAB0ASY+xVAClQj7X7J4CXNPW+45yfra/vo7KyknrnCCFWycbGhiZxNaKkpCScOHECmZmZiI6Oxt27dwV1OnbsiGnTphntOIiIiLB4IqdFCR0xG+c8gTE2D8DnAEI0j6pOAwjT9Mrpn5vFGJsKIAlAKwDrRM7dAnVyV69oAgQhxBrRz66Gp03iOOfgnOu279Ju56UlkUgwduxYDBs2TLB9l1ZAQEC9JXMAJXSkhjjnyxlj+wG8CmA0AG+otwNTQn2b9TvOeZmRc48zxvoAeAPAVADtod4d4hyAHwGsMbK+HSGEENKg1q5di9RU9Uij0tJSJCUl4exZ4Q0kV1dXREVFoV27dqLtWGpZkupQQkdqjHN+EsCsWp57D+qxdm9aNChCCCHEQpKSknTJ3J07dxAdHY379+8L6vXs2ROhoaGws7MTlAGWXZakOpTQEUIIIYTAcB9WzjkOHz6M7du3Q6VSGdSzsbHBpEmT0L9/f4Nb4YwxcM7BGMPAgQMbLJkDKKEjhBBCCDG4xfrgwQPEx8fjr7/+EtRr1aoVoqKi0KpVK4PjDdkbJ4aWLSGEWFRaWhree+89BAYGol27drC1tYWTkxM6deqERx99FD///DOKiooaO8wmY82aNWCMNfsB76NHjwZjDLNnz260GHbv3q37rNPS0hotDtL06N9iTU1NxcqVK0WTuYCAADz33HNNLpkDqIeOEGIh5eXlWLhwIb7++muUlRnOiykrK0NhYSGuXr2KP/74A6+99ho+/vhjPP30040ULWlOli5dimXLlqFDhw6UqBGzKZVKJCcn69aRq6ysxJ49e7B3715BXTs7O0ydOhU9evQwOC6TyRAaGlrvEx7MQQkdIaTOioqKEBoaip07dwIAOnXqhJdeegljxoxBmzZtUF5ejps3b2Lr1q348ccfkZaWhuXLl1NCRwhpFEqlEgkJCbp1/XJzcxETE4ObN28K6rZv3x6RkZFwdXU1OK5QKBASEtIkkjmAEjpCiAW89NJLumRuzpw5+OqrryCTyQzqtGnTBoMHD8bChQvx2WefITY2tjFCJY1k9+7djR0CRo8eLdg4nbQ8SqUScXFxumvhwoULSEhIQEmJcIvykSNHYvTo0QYL0je1RE6LEjpCSJ2kpKRgzZo1AIDQ0FCsWrXKZH25XI7FixcjPDy8/oMjhBA9+rNYy8vLsW3bNhw7dkxQz9HREREREXjooYcMjisUCixYsKBBYq0pmhRBCKmTTz/9FIB6pfTly5ebfV7VsSiA+i9n7YQKLy8vyGQyuLm5YfDgwXjvvfeQm5trtL3Zs2eDMYbRo0cbrZOWlqYbFC/WY1RWVoavvvoKgYGB8PDwgEwmg6enJ7p3746oqCh89913ohM66hJ3bSUlJem+F6VSabLu5s2bdXX1F0blnOPIkSNYvHgxBg8eDDc3N8hkMnh4eCAwMBDLly8X3XRcq+pnvnfvXkRGRqJt27awsbExSNpNTYqobRzaSQ7Lli0DAFy/fl33fWof+u9n7qSImJgYhIaGwtvbG7a2tmjVqhWCg4Pxyy+/GO3hq3ptlZaW4uOPP0afPn3g4OAAV1dXBAUFITk52ej7kvqjVCrx7rvv6pK5zMxM/O9//xNN5jp16oQXXnhBkMxJpVKEhIhtkNQ0UA8dIaTWCgsLsWPHDgDA2LFj4efnV+u2zpw5g379+gmO5+bm4ujRozh69Ci+//57bNu2DZ07d671+xhTUFCAsWPH6n7ga2VnZyM7OxsXL15ETEwMAgICDOJsrLiDg4Ph6emJrKws/Prrr/jwww+N1v31118BAL169UKfPn10xzdu3CjaU3r//n3s27cP+/btw48//oht27bBy8vLZDxfffUV5s2bJ1ivyxyWjKMuSkpK8PjjjyM+Pt7geGZmJrZt24Zt27ZhzZo1iIuLg5OTk9F2CgoKEBgYiKNHjxoc37lzJ3bu3InVq1fj+eefr49vgYiourbcqVOnkJycLLp917hx4zBkyBDB9l0NtdtDXVAPHSGk1g4dOqQbVDxy5Mg6tcUYw5AhQ/DZZ59h//79uHr1KrKysqBUKrF8+XL4+voiLS0Njz32WL2Mg/roo49w/PhxSKVS/Pvf/8bp06eRkZGB69evY//+/fjiiy8wYMAAwfIijRW3jY0NHnnkEQDA+vXrjbZdVFSkS1CeeOIJQRtBQUH45ptvcPDgQaSlpSEzMxOnT5/Ge++9Bw8PD5w+fRpz5swxGcvFixcxf/58BAYGIiUlBZmZmbh69SpeeeUVs7+X2sQxcuRIFBQUYNGiRQAAX19fFBQUGDxWr15tVgwA8OKLL+o+q8cffxzHjh1DVlYWTp06hWeffRaAeojBU089ZbKdV199FRcvXsQXX3yBa9euISsrC5s3b9b1+MyfPx8ZGRlmx0VqTz+ZKykpQUxMDDZu3ChI5tzc3PDMM88I9mKNiIjAkiVLMG/evCadzAGAbsNZetDD2h4DBw7kNXX+/HmT5QCa9cPSVq9erWv7999/t3j7+tLT07mbmxsHwLdv3y4onzVrFgfAR40aZbSN1NRUXby7du0yKOvfvz8HwOfPn9+gcf/444+1/vc5cOCA7tz9+/eL1vn11185AM4Y42lpaTVq/9y5c9zGxoYzxvjly5cF5drPHAAfO3YsLy8vN9rWqFGjOAA+a9asGsVgThxLlizhAHiHDh1MtrNr1y5dvKmpqQZlx48f15U999xzoucvWLBAVyc5OdmgTP/asrGx4YcOHRKcf/bsWV2dFStWmP6mjajuZxj526ZNm/jSpUv50qVL+bPPPstdXV1Ffy727t2bL1y4UFdX+/j4448b+1sQAHCcG/mdSD10hJBay8nJ0X3t4uJSr+/l7e2NcePGAVD3kliatqexTZs2Fm23PuMeNmwY/P39Afx9W7WqdevWAQBGjBiBDh061Kj9Hj16oH///uCc62YxG/PJJ5/AxqZ+RvHUJI7a+uGHHwAA9vb2unGhVb3zzju6BWW///57o2099thjGDJkiOB47969dbfnxcZuEctRKpU4fvw4VCoVDhw4gB9++EEwllUmkyEsLAwRERGie7E25fFyYiihI4TUmvoPRjVL7HSgUqmwfv16hIeHw9fXFwqFwmCA+4YNGwAAly9frvN7VaX9Rfvpp59i8+bNNRoL1phxz5gxAwDwxx9/oKKiwqAsOzsb27ZtAyC83apVVlaGb7/9FhMnTkSbNm1ga2trELs28TAVu5eXFwYOHFin78MScdTF/v37AQDjxo0z+seJra0tQkNDAQD79u0z2lZwcLDRMu04ynv37tU2VFINpVKJ2NhYFBYWYt26daJ7sbZu3RrPP/+8YC9WrYCAgKZ/i7UKmhRBCKk1d3d33dd1nclZUFCAKVOmiK7SXlVeXl6d3kvM0qVLER8fj4yMDEyePBkeHh4YNWoURo4ciXHjxqFXr16i5zV23E888QTef/99ZGVlYdu2bZg0aZKu7I8//kB5eTlkMhmmT58uODc9PR3jx4/HuXPnqn0fU7Frewlry1Jx1MX169cBiM++1tezZ08A6oSsrKwMcrlcUMfHx8fo+fb29gBgcvYwqR398XJXr15FbGwsHjx4IKj38MMPY/z48QZrZcrlcpSVlVnF5AdjKKEjRI9+jxOpnv4v8osXL9aprfnz52Pv3r1gjOGZZ55BVFQUunbtChcXF90P3jlz5uDXX38V9ERZgr+/P06cOIGlS5ciNjYW2dnZiI2N1S2A3LdvX3zyySeYMGFCk4q7e/fu6N+/P06dOoVff/3VIKHT3oYNCQkxSL61nnrqKZw7dw4ymQyvvPIKpkyZgo4dO8LJyUl3+3TixIk4cOCAydgVCkWdvgdLxVEXhYWFANTrj5miP7u1oKAAHh4egjr6i9AaQz9r6k6pVCIlJQV5eXmQyWQoLy9HZWUldu3apetx1adQKBAWFoZu3boZHPf398fMmTMbKux6QwkdIaTWhg4dCqlUisrKSpO3oKrz4MED/PLLLwCARYsW4f333zdazxhzbvlWlwx06tQJv/zyC0pKSnD06FEcPHgQ27dvx+7du3HmzBlMnDgRGzduxJQpUywWtyU88cQTOHXqFBISElBUVAR7e3vcuHEDBw4c0JVXdeXKFd2YvhUrVuC5554TbVub6NSXphKHo6Mj8vLyqn0f/XJTS5eQ+qVUKpGYmKibrVpeXo6cnBzExMTg1q1bgvq+vr6IjIwU3E6PiIiwyt44MTSGjhBSa46OjroB/zt37qz1xuiXLl1CaWkpAOiW4hDz559/Gi3TDmo2dSsrPT3drHjs7OwQGBiIhQsXIiUlBX/++SdatWoFzjneffddi8ZtCY8//jgkEgkKCwuxceNGAOreOc45nJycdOO+9OkvMGws9vLy8nobs9bU4tCuoXjhwgWT9bS3hb29vUVvt5KGkZKSYrD0yLlz57Bq1SpBMscYw6hRozBr1qxmncwBlNARQurojTfeAKCeGGDuumMAcP78ed3X2qQI+Hu2aVVHjx7F1atXjbbn7e0NQN3jY+x21vbt282OT1/37t3x+OOPAzC8tWyJuC2hTZs2ut0atLNatc8RERGit0TNiT0uLq7ex3pZIg7trW1j55tjxIgRAIAdO3YgPz9ftE5ZWRkSExMN6pPGoR1Lqf032bBhg8G1BKh7UGfNmoUxY8YIboNb46SH6lBCRwipk3HjxukWWk1MTMQLL7wgWLRTX1lZGT744AODMSv6O0xof2HqKyoqwosvvmgyjkGDBgFQ7y6wZcsWQXlqaqrJrcmqGwOoTcr0x0xZIm5L0d5W3bp1K3bv3q3rFTQ2u1V//KNY7BkZGQ2yZ6Ul4tD+m2RlZdV6jN3TTz8NQH17fOHChaJ1lixZopud+swzz9TqfUjNKJVKfPnll1i2bBk++OADvPPOO7qt3jIyMvC///0PJ06cEJzXpUsXvPDCC4LdaxQKBSIiIjB58uSGCL9BUUJHCKmzlStXYtSoUQCA1atXo0ePHvjvf/+Ls2fPIisrC+np6Thy5AjeffdddO3aFW+99ZbBL14fHx9dj8cHH3yADz74AFeuXEFmZiY2b96MESNG4NSpU+jSpYvRGMaPH69bQ2727NnYsGEDsrKycPv2baxZswYjRowQHcCu1aNHD4wfPx6rVq3CqVOnkJmZiYyMDBw5cgTPPPMMNm3aBEC9xpgl47aUyMhI2Nraory8XLd/qbe3N8aOHStaPyAgQLcu3SuvvIKVK1fi+vXruHv3Ln7//XcMHToUmZmZNV67rqYsEceAAQMAqHcCeOedd3D37l1UVFSgoqLC7OVnBgwYoEvqVq5ciSeffBInTpzA/fv3cfbsWcyZMwcfffQRAGDq1KmYOHFiXb5tYgbtODltb1x5ebluEd3jx4/j22+/RWZmpsE5UqkUEydOxOOPPw4HBwfdcRcXF0RERGDBggXNrmdOx9iKw/SgR1N/1MdOEaT2SktL+auvvsplMlm1O1a0bt2ar1271uB8pVJpdCV3xhj//PPPq90NIikpyej7d+nSxWBnhao7RVQXMwAeHBzMi4qKLBp3XXaKqCoyMtLg/efNm2ey/vbt27mtra1o7HK5nP/2228md3gwZ3cOLVPt1DUOzjkfMmSI6Pn69U3tFME558XFxTw8PNzkNRAUFMTz8/MF55rahURfTT4zMS3pZ9gXX3wh2L3hX//6F+/Ro4fov427uzufM2eOru7777/Pz54929jfhkWBdooghNQ3uVyOL7/8EpcuXcKyZcswYsQI+Pj4QC6Xw8HBAZ06dcKjjz6KX375BWlpaYL9MHv16oXjx4/jqaeegre3N2QyGXx8fBAeHo6dO3fitddeqzaGSZMmYe/evQgNDYW7uztsbW3RuXNnLF68GMePHze5C8SJEyfw0UcfITg4GJ07d4ajoyPkcjnatm2L0NBQ/P7770hOThaMR7NE3JZS9faqdtFhY8aNG4dDhw5h2rRpcHd3h1wuR/v27fHkk0/i8OHDePTRR+szXIvGsXnzZrz22mvo1q2b6Kr/5rCzs0NcXByio6MxefJktG7dGjKZDF5eXhg/fjx+/vlnbN++nWa3NpCqaw7evHkTq1atMhh/q9W3b1/MmTNHtwagQqFAaGho8+2NE8HUCR8h1icgIIBrF5E014ULF9C9e/d6iogQQupXS/kZtnbtWqSmpgKAbvuunTt3omrOIpPJMGXKFPTt21d3LCAgoFmOkQMAxtgJznmAWBmtQ0cIIYSQJiMpKUmXzBUUFCAuLg7Xrl0T1PPx8UFUVJTB2FiJRNJsk7nqUEJHCCGEkCZBf/uuv/76C3FxcSgqKhLUGzJkCMaNG6fbSUQrPDy8IcJskiihI4QQQkijUyqVOH78OCoqKrBz504cPHhQUEehUCA8PBxdu3Y1OG7Ne7BaCiV0hBBCCGlUSqUScXFxuH//PqKjo3Hnzh1BHT8/P0RERMDZ2dnguIuLC+bNm9dAkTZdlNARQgghpFEolUps2rQJZWVlunXnysrKDOowxjB69GiMHDkSEonh4hwSiQRBQUENGXKTRQkdIYQQQhqcdiZrWVkZkpOTcerUKUEdZ2dnREZGii4sLZfLMWXKlBZ9m1UfJXSEEEIIqXdKpRIpKSkG68vdvXsX0dHRyMrKEtTv1q0bpk6dCnt7e4PjCoWiQbalszaU0BFCCCGkXimVSiQkJKCyshKAepeqY8eOYevWrbpjWlKpFMHBwRg0aBAYY4KykJCQBovbmlBCRwghhJB6lZycrEvcioqKsHHjRly8eFFQz8PDA9OnT4e3t7egjGaymkYJHSGEEELqVXFxMQDg+vXriImJQX5+vqBO//79ERISArlcbnCcbrGahxI6QgghhFicUqlEcnIyiouLoVKpsG/fPuzevVuwfZdcLje67yrdYjUfJXSEEEIIsSj9HR/y8/MRGxuLtLQ0Qb02bdogKioK7u7ugjKFQoGQkBC6xWomSugIIYQQYjH6ydzly5cRHx8vun3XsGHDMHbsWMH2XTKZzGiPHTGOEjpCCCGE1In+7VUAqKiowI4dO3D48GFBXXt7e0ybNg2dO3c2OB4QEIDJkyc3SLzNESV0hBBCCKk17bZd2rFx2dnZiI6ORnp6uqCuv78/IiIi4OTkZHCcMUbJXB1RQkcIIYSQWktOTtYlc2fOnEFSUpLo9l1jx47F8OHDBdt3AcDAgQMbJNbmjBI6QgghhNSKUqlEcXExSktLsXnzZpw5c0ZQx8XFBZGRkfD19RWUMcYwcOBA6p2zAEroCCGEEFIj+mPm0tPTER0djezsbEG97t27Y+rUqVAoFAbHIyIiaNKDhQn7PQkhpA5OnTqF119/HX379oWnpydsbW3Rrl07BAUF4YsvvkBOTk61bTDGwBjDmjVr6j9gImrNmjW6f4f6Mnv2bDDGMHr06Hp7D2J5SqVSN3P18OHD+O677wTJnI2NDaZMmYJHHnlEkMz5+/tTMlcPKKEjhFhEUVERZs+ejYEDB+I///kPzp49i+zsbJSVleH27dvYuXMnXnvtNXTu3Blr165t7HBJM7d06VIwxuDn59fYoTQ7ycnJKCgowPr167FlyxbBXqxeXl547rnnEBAQIPiDQCaTYebMmQ0ZbotBt1wJIXWWl5eHiRMn6pYoGDx4MP75z39i6NChcHFxwe3bt5GUlITPPvsM2dnZmDVrFtLT0/Gvf/2rkSMnhJhDqVQiJSUFeXl5SEtLQ0xMDAoKCgT1Bg4ciODgYMH2XQAgkUgQGhraEOG2SJTQEULq7IUXXtAlc/Pnz8dnn31mMJPNw8MDffr0wT/+8Q+MGzcO586dw6JFi9CvXz8EBwc3VtiEEDOsXbsWqampqKysxN69e7F3717B9l22traYOnUqevbsKdoG7fpQ/yihI4TUydatW/Hbb78BAKZMmYL//Oc/Rut6e3sjOTkZPXr0QGFhIZ5//nlcvXpVsFI8IaRpSEpKQmpqKvLy8hATE4MbN24I6rRt2xZRUVFwc3MzOE4THxoWjaEjhNTJF198AUA9keHLL7+stn779u3x+uuvAwBu3LiB6Ohok/XLy8vx+eefo3///nBycoKrqyvGjBmD2NhYk+edPn0aTz/9NDp37gx7e3soFAr4+vpiyJAhWLhwoW5rIjF37tzBv/71L/Tt2xcuLi5QKBTo1KkT5s6di2vXrhk9z8/PD4wxLF26FIB6YsGoUaPg6emp+3zefPNNMMbg7e0tGHtU1YIFC8AYQ6tWrVBRUSEoz83NxbvvvouHH34Y7u7usLW1RYcOHTBz5kycPn3aZNsAEBMTg9GjR8PV1RVOTk7o378/Pv/8c5SXl1d7rjnKysrw6aefok+fPrC3t4enpyeCgoIQFxdX7bmccxw5cgSLFy/G4MGD4ebmBplMBg8PDwQGBmL58uW6XQn07d69G4wxLFu2DABw/fp13eQO7WP27NkG5yiVSrz33nsIDAyEl5cXZDIZ3NzcMHjwYLz33nvIzc21xMdhdbRbeF28eBGrVq0STeZGjBiBp59+WpDMAaBkrqFxzulBD6t8DBw4kNfU+fPna3wOMS4/P59LpVIOgI8ePdrs865fv84BcAD8kUceEZRry1avXs0DAwN1r6s+Xn75ZdH2165dq4vL2CMsLEz03A0bNnB7e3uj59nZ2fHo6GjRczt06MAB8LfffptPnz5dcO4XX3zBT506pXu9ZcsWo5+RSqXi7du3N/p97t27l3t6ehqNUyKR8OXLlxttf+7cuUbPHTVqFF+9erXudW3k5eXxwYMHG32PhQsX8lmzZuner6r4+HiT/34AeL9+/XhGRobBebt27ar2vFmzZunqnz59utr6fn5+/PLly7X6HCytIX6GnT17li9btoy/9dZb/OGHHxb9TBwcHPhTTz3Fly5dKvp4//336z3OlgjAcW7kdyL10BFCau3w4cO6XqZRo0aZfZ6vr69ukdEDBw4Yrffhhx9i//79eP3113H+/HlkZmZi165dGD58OADg66+/xurVqw3OycnJwdy5c1FZWYmAgADEx8cjNTUVWVlZUCqVSEhIwOzZs+Hi4iJ4vx07duDRRx9FUVERRo4ciY0bN+LOnTvIyspCSkoKRo8ejZKSEsyYMQOnTp0yGvf333+PDRs24IUXXsDJkyeRlZWFU6dOYfTo0ejXrx969OgBAPj111+NtrF3717cvHkTAPDEE08YlJ07dw7BwcHIyspC7969sX79ety4cQPZ2dk4dOgQIiMjoVKp8MorryApKUnQ9ooVK7By5UoAwMiRI7F7925kZWXh3LlzmD9/Pvbu3YsPP/zQaGzmeOaZZ3DkyBEAwHPPPYczZ84gKysLBw4cwMSJE/HRRx9hz549Rs+3sbFBUFAQvvnmGxw8eBBpaWnIzMzE6dOn8d5778HDwwOnT5/GnDlzDM4bOXIkCgoKsGjRIgDqa62goMDgoX/NMMYwZMgQfPbZZ9i/fz+uXr2qu1aWL18OX19fpKWl4bHHHoP692nzolQq8cknn2DZsmW6R2xsLDIzM/Hdd9/h6NGjgnM6duyIuXPnomPHjkbbpckPjcBYpkcPejT1B/XQNT79XpzffvutRudOnDhRd25ZWZlBGfR6Aj7++GPBuSUlJXzQoEEcAHd3d+fFxcW6soSEBA6AS6VSnp2dbXY85eXl3NfXlwPgwcHBvLKyUlCnoqKCjx07lgPgEydOFJRre+ig6aUz5v333+cAuJOTk0Hs+p5//nkOgD/00EOCsmHDhnEAvE+fPryoqEj0fG3vV/fu3blKpdIdLyoq4q6urhwAHzJkCC8tLRWc+9577xn8G9TUoUOHdOfOnz9fUF5RUcGDg4MNegRr6ty5c9zGxoYzxkR7z5YsWcIB8A4dOtS4bX3p6enczc2NA+Dbt2+vU1uWYMmfYWfPnuXvvvuuQc/akiVLeFhYGJfJZKK9vuPHj+dvv/220Z65L774gp89e9ZiMRJDoB46QpoWpVKJL7/8EsuWLcOXX34JpVLZ2CHViv4iwa6urjU6V7++scWG9cfb6bO1tcXHH38MALh//z4SExN1ZdoeQwcHhxrFlJCQgBs3boAxhh9++EF0v0mpVKobm7V161bcv39ftC0PDw+89dZbRt9rxowZYIyhoKDAIHat8vJy3djCqr1zJ06cwMGDBwEAq1evFizaqvXee+8BAC5cuGAwnm7jxo26MWEff/yx6PIS//rXv9C2bVuj8Vfnp59+AgA4OTnh3XffFZRLpVLd2Mva6tGjB/r37w/OOXbu3Fmntkzx9vbGuHHjAAApKSn19j6NITk52WAcZ2lpKWJjY5GQkCAYR+nq6oqnn35adC9WxhgiIiKwZMkSzJs3j8bONRJK6AhpYEqlEomJicjLywOgXsMtMTHRKpM69R+MajXdUUD/XGOmTJkCqVQqWjZ69GjdQGz927Z9+vQBYwz5+fl47rnnkJ6eblY82qSge/fucHZ2RmFhoeija9euuvhPnjwp2tbYsWNFEyUtPz8/DBs2DACwbt06QXlycrIuWZwxY4ZonM7OzrrZwmIPV1dXeHl5AVAngVraZNDd3R0jR44UjU+7yn9tad9j3LhxcHBwEK3TvXt33WdpTFlZGb799ltMnDgRbdq0ga2trcHkhmPHjgEALl++XOtYAUClUmH9+vUIDw+Hr68vFAqFwfts2LDBIu/TlGj3YNW6ffs2Vq1aJfpzqGfPnnjhhRfQrl07QZlMJsO0adMoiWsCaK0AQhpYSkqK4K/f8vJypKSkWN0PRXd3d93X5mzppU+b0AIQnSEHAN26dTN6PmMMXbp0wZEjR3D9+nXd8Y4dO+Kll17C119/jR9++AFr1qxB//79MXLkSIwaNQrjxo2Do6OjoL1Lly4BAM6fPw8nJyezvofMzEzR4/7+/tWe+8QTT+DAgQNITk5Gbm6uQW+iNskbOHCg4DPQxpmfny86DrC6ONPS0gAAXbt2NZmEm/rsq6N9j+ra6Natm+77qSo9PR3jx4/HuXPnqn0//WuppgoKCjBlyhTs3bu3Xt+nqYmPjwegTmYPHz6MHTt2QKVSGdSxsbHBpEmT0L9/f4NrRaFQoLi4GC4uLggKCrK6n1vNFSV0hDQwY78UrPGXhf62ShcvXqzRuRcuXACgXsNKJpOJ1jHWu6OlTcwKCwsNji9fvhy9evXCf//7X1y4cAEnTpzAiRMn8OWXX0KhUOCZZ57BBx98YJC41ebzLy0tFT1u7DaovkceeQSvvvoqysrKEB0djWeffRYADG7DVr3daok4Hzx4AMD8z7Y2LPEeTz31FM6dOweZTIZXXnkFU6ZMQceOHeHk5KRbt3DixIk4cOCA6JIu5tJOAmGM4ZlnnkFUVBS6du0KFxcX3XU5Z84c/Prrr3V6n8akv8sDY0zXO/7gwQPExcXhypUrgnNatWqFqKgotGrVyuA4rS3XdFFCR0gDc3FxEf2lbG5vS1MydOhQSKVSVFZWmpyxWNXNmzd1vWraGatitImBMdpErmpiwBjDnDlzMGfOHKSlpeHgwYPYu3cvEhMTcefOHXz99dc4duwYDh48qBsPpE0+wsLCdL0X9cnDwwPBwcHYtGkTfv31V11CFxcXh+LiYkgkEjz22GOC87Rx9u3b16y15oydb+5nWxsODg7Iz8+v9XtcuXJFN15txYoVeO655yweI6D+DH755RcAwKJFi/D+++8brWettEM8tHcFtMnctWvXEBsbK/oZDho0CBMmTDD4Q0sikSA8PJySuSaMxtAR0sCCgoIEPVIymQxBQUGNFFHtOTk5YezYsQCAPXv2IDU11azz1qxZo/t62rRpRuuZ6vXjnOvGNHXo0MFoPT8/P8yYMQOrVq3C9evXMX/+fADAkSNHsHXrVl29hx56CABMLkdiadoeuD179uD27dsA/l7KZOzYsfDx8RGco43z0qVLogvrVkfbq3rp0iWT4xhr2uMq9h7VtWGs/OzZs7qvH3nkEdE65eXldR7TdunSJV3vpbH3AYA///yzTu/TmKoO8aisrERKSgrWrl0rSObs7OzwyCOPYPLkyQY/o1xcXCiZswKU0BHSwHr37o3Q0FBdj5yLiwtCQ0Ot9oelNkFSqVS6r025ffs2Pv30UwDqWaxRUVFG627atMnobgp79uzRjdsz1cunz8bGBm+//bbutX5CMX78eADq3St27dplVnt1NXXqVDg6OkKlUuG3335DRkYGduzYAUD8dqt+nCUlJbot12pCOxnj/v372Ldvn2idiooKbNq0qcZtV32PHTt2GO3dunjxotHxc/q3iI39+2t7Mo3RJiSmduMw532OHj2Kq1evGm2jqdO/G5Cbm4s1a9aI/ru3b98eL7zwgm6NRAA0c9XKUEJHSCPo3bs35s2b1yx+WIaEhGD69OkA1Et/vPnmm4LB1VoZGRmYNGkSCgoKwBjDt99+a3If15s3b+Lzzz8XHC8tLcWCBQsAqCdm6C9impqairKyMqNt6v9y9vDw0H0dGRmJ9u3bA1CPmbp3757RNgAYTUZqwt7eXtdD+euvv+L3339HZWUl7OzsEBERIXrO0KFDMXjwYADqrcGq66WqGufUqVN1EzD+9a9/iX5Wn3zyia7HsDZmzZoFQD0e8N///regvLrkX39SidiyLhkZGbp/f2O0/7ZZWVlGx77pjwEVe5+ioiK8+OKLJt+nqdOO5zx//jxWrVqlW6xa38iRIzF79mzddSGRSGisnDUytkAdPejR1B+0sHDTkZOTo1voF5oFa9evX8+vXbvGs7Ky+NmzZ/mHH37IPTw8dHU+/PBDo+1p6/j5+XGJRMLfeOMNfuHCBZ6VlcV3797NR4wYoauzatUqg3OXLFnCvb29+bx583hSUhJPS0vjOTk5/OrVq3zNmjW6xYOdnJwE20alpKTotgzz8fHhX3zxBT9//jzPycnh6enp/PDhw/w///kPHzx4MO/Vq5cgbu3CwkuWLDH7s9uyZYvue9GeP336dJPn/Pnnn9zR0ZED4C4uLvydd97hp0+f5tnZ2fzevXv8xIkTfOXKlTwoKIg7OTkJzv/666917xkYGMj37NnDs7Ky+Pnz5/n8+fM5Y4z7+fnVaeuvqKgo3fnPP/88P3v2LM/OzuYHDx7kISEhBt9v1YWFy8vLdWXOzs78m2++4WlpaTw9PZ3/9ttv/KGHHuL29va6OvpbeWkdOXJE9/7//ve/eXp6Oi8vL+fl5eUGi0ZrryW5XM7ff/99/tdff/GMjAyelJTE+/fvzyUSCe/SpUutF0C2tJr8DPvpp5/4W2+9xQMCAkS373J0dOQzZ840WBj4448/poWBmzCYWFi40X8p04MetX1QQte0FBQU8CeffJIzxkzui+nu7s7XrFljsi39ZG3kyJFG2xLb41S7Q4Cph729PU9MTBR976SkJN1OCqYeAwYMEJxbm4SuoqKCt27d2qDt+Pj4as87cuQIb9euXbVxuru7i55vai/XkSNH1vtergsWLDC5l+v27du5ra2t6LlyuZz/9ttvfNSoUUYTOs45HzJkiOj5+vWVSqXRf2/GGP/8889NxtnQjP0MO3v2LP/iiy90SdnSpUv5Sy+9xFu1aiX6vXXu3Jm/+eabgp0eSNNmKqGjW66EEItwdHTEzz//jOPHj+tuI7u5uUEul8PHxwdjxozB559/jitXruhuyVXH1tYWKSkp+Oijj9CnTx84ODjA2dkZo0ePRnR0NL766ivBOfPmzcMff/yB559/HgMGDICPjw9sbGzg5OSEfv364c0338TFixeNLpw7adIkpKam4sMPP8SIESPg4eEBqVQKR0dHdO/eHTNnzkR8fLzJPWhrQiqV4tFHH9W9dnNzQ0hISLXnPfzww7h8+TK++uorjBs3Dq1atYJMJoO9vT06deqERx55BL/++qtuTbiqvvnmG2zYsAGjRo2Cs7MzHBwc0KdPH3z00UdISUkxuTCyOZydnbF37158/PHH6N27N+zs7ODu7o7Ro0fjjz/+0O30Ycy4ceNw6NAhTJs2De7u7pDL5Wjfvj2efPJJHD582OAzM2bz5s147bXX0K1bN9jZ2YnW6dWrF44fP46nnnoK3t7ekMlk8PHxQXh4OHbu3InXXnutVt9/Q1IqlYiPj9eNlysqKsKJEyewevVqZGRkGNSVSCQIDg7G448/LlhWpqaLg5OmhakTPkKsT0BAAD9+/HiNzrlw4QK6d+9eTxERQkj9EvsZ9t577+kmdZSUlCAxMVF0QWY3NzdERUUZ3dYtICAAkydPtnzQxGIYYyc45wFiZbQOHSGEEGKllEqlLpm7desWoqOjdXv16uvduzcmT54s2lPJGMPAgQMpmbNylNARQgghVkapVGLTpk0oKyuDSqXCwYMHsXPnTsEMc5lMhsmTJ6Nv374Gt1RpoeDmhxI6QgghxIqsXbtWt4h3YWEh4uLiRNfK8/b2RlRUFDw9PQ2O0x6szRMldIQQQkgTVVRUhIKCAt1t1dzcXF0yd/XqVcTGxoou3vzwww9j/PjxBjs++Pv7Y+bMmQ0TOGlwlNARQgghTVBRURHy8vJQdfJiZWUldu7cKTrTWqFQICwsDN26dROU3b9/v95iJY2PEjpCCCGkicnNzUVRUZHguEqlwo8//ohbt24Jynx9fREZGanbVrAq/W3ASPNDCR0hhBDSRBQVFYnOUgWA4uJiFBYWCpI5xhgCAwMRGBgIqVRqtG1jiR5pHiihI4QQQpoAY71ynHPk5eWhqKhIcPvVyckJkZGRBvvSipHJZAgKCrJkuKSJoYSOEEIIaUSmeuXKy8uRk5ODiooKQVmXLl0QFhYm2PEBAORyORQKBfLy8mhWawtBCR1pcTjntMUNIaRJMNYrBwAPHjxAfn6+Qa8c5xxSqRTjx4/H4MGDRX+WyWQyTJkyhRK4FoYSOtKiSKVSVFZWwsaGLn1CSOPKyspCWVmZ4LhKpUJubi5KSkoEZSUlJXj22Wfh4+Mj2ib1xrVc9FuNtCj29vYoLCyEq6trY4dCCGnBjCVzZWVlyMnJ0a07p8/GxgYlJSVGkzkAmDdvniXDJFaEEjrSojg7OyMrKwtOTk4mZ4MRQkh9MDZejnOOwsJCFBQUCMoYY3B0dEROTg7u3LljtG2axdqyUUJHWhQnJycUFxfj+vXrcHd3h6OjI6RSKY2pI4TUO2O9cpWVlcjNzUVpaamgzMbGBnK5HNnZ2Th16hQKCwtF26ZZrIQSOtKiMMbQqlUrFBQUID8/HxkZGaK3NgghxJIKCwtFZ6pWVFSILkeinfygUqlw9+5dZGRkoLi4WLRthUKBkJAQGjfXwlFCR1ocxhicnZ3h7Ozc2KEQQpo5pVKJTZs2CXrmKioqkJKSgkOHDgnOsbe3R3h4OLp06WKybZlMhtDQUErkCABK6AghhJB6oVQqERcXJ+h9u3//PqKjo0XHw/n5+SEiIqLaPzhdXFxoAgQxQAkdIYQQYmFJSUk4fvy44LhSqURiYqKgx44xhtGjR2PkyJGQSCQm26bxckQMJXSEEEKIBa1duxapqakGx8rKyrB582acPn1aUN/Z2RmRkZHo0KGD0TblcjnKysponTliFCV0hBBCiAUolUokJycLJi/cvXsX0dHRyMrKEpzTrVs3TJ06Ffb29kbbDQgIwOTJky0eL2leKKEjhBBC6kipVCI+Ph4qlUp3jHOOY8eOYevWrYLZ9FKpFMHBwRg0aJDRZZMcHR3x+uuv12vcpPmghI4QQgipA7FbrEVFRdi4cSMuXrwoqO/p6YmoqCh4e3sbbdPT0xMvvfSSxWMlzRcldKTWGGOdACgB2GkO/YNzvsZE/dYAXgcwFYAvgCIA5wCsAbCGV50KRgghTdyyZcsEx65fv46YmBjk5+cLyvr374+QkBDI5XKjbdItVlIblNCRuliJv5M5kxhjAQCSALTSO6wAEKh5PMIYC+ecC5dKJ4SQJkSpVCIlJQV5eXkGx1UqFfbt24fdu3cLliqRy+VmrRlHyRypLUromgHGmB2AYAADAXgBkHPOn9ErlwFwAlDJOc8Tb6XG7/kkgHEAUgH4V1PXE8BGqJO5bACvAkgB4ALgnwBeAjARwFcAnrdEfIQQYmnGJj0AQH5+PmJjY5GWliYoa9OmDaKiouDu7m60bblcjilTptDsVVJrlNBZOcbYCwDeBaD9ScEAcADP6FXzAXAFAGeM+XDO79fxPd0AfA6gEsB8APHVnLJQE4MKwBTO+WHN8bsAXmaMSQDMBfAsY+wrzrmyLvERQoilKZVKJCQkiG4VeOnSJcTHx4smesOGDcPYsWNhY2P8121ERAQlcqTOTK9eSJo0xthHAFYA8ADwAMBJsXqc8xsANkOdwIdZ4K0/hbq37SsAZ6qJUQbgWc3LBL1kTt8SAOVQJ6NzLBAfIYRYlFgyV1FRgeTkZKxfv16QzNnb2+OJJ57AhAkTTCZzCoWCkjliEZTQWSnGWCCABZqXy6C+1TrGxClxUCdMdUroGGMjADwNIB3qRKw6I6G+tQoA0WIVOOeZAPZoXk6tS3yEEGJpK1asECRz2dnZ+O6773DkyBFB/Yceeghz585F586dTbYrlUoREhJi0VhJy0W3XK3Xy5rn1ZzzZQDAGDP176ndg6bWfwpqettWQ50YvsY5z2eMGR8UojZQ72vhTz7DsnEA2jPGPDnnwhU4CSGkARnbi/XMmTNISkoS3b5r7NixGD58uNHtuxQKBYqLi2nHB2JxlNBZr+FQj5VbZWb9u5pn4wsfVW8BgB4AUjjnv5l5ThfNswrAdRP19Bdx6gqAEjpCSKMRW1uutLQUmzdvxpkzwpEmLi4uiIqKQvv27Y22SWPlSH2ihM56eWqeTSVJYmp1m50x1hHA/wEog3pWqrm0ceZwzitM1MvQ+9qjhuERQojFJCUlCZK5O3fuIDo6GvfvC+eU9ejRA6GhoVAoFEbbDAgIoGSO1CtK6KxXPtQzW90AmLMUyUOa5+xavp92zbkPOOeXanCeg+a5pJp6+iOKHWsSGCGEWIpSqcTx48d1rznnOHLkCLZv3y4YR2djY4OJEydi4MCBRrfvkkgkCA8Pp2SO1DtK6KzXeQAjoJ4I8aMZ9adrno+brCWCMTYDwHgAaQDeq+npmufqdoHQ/2lotC5j7Hlo1qrz9fWtYSiEECJOqVQiNjbW4NiDBw+QkJCAy5cvC+p7eXkhKioKrVu3NtomLRJMGhIldNYrDuoZpP9mjG3knBvteWOMDYF6AV8OIzNNTZzrCuA/mpevcM6FCy2ZVqh5Nn4vQk1/x4kHxipxzr8F8C0ABAQE0FZhhJA6SUpKwokTJwQTH1JTUxEbG4uCggLBOQMHDkRwcLDJ7btovBxpaJTQWa9VAF4B0AHAacbYawAM1nhjjHUAMBvqyQy2AP4E8GsN32cJgNYANnLOE2sRp3ZygytjzMbEODovva9re1uYEELMJjbxobKyEnv37sWePXsE9W1tbTF16lT07NnTaJv+/v6YOXOmxWMlpDqU0FkpznkJY2wKgF0A2gL4DeoeOA4AjLH7+Hv9NwbgDoBpnHNVDd9Ku63XVMZYdT1iPzLGtLd//TnnaQC09yqkAHwBXKvmfQCgJmP0CCGkxlasWIGsLMPJ9Hl5eYiJicGNGzcE9du1a4fIyEi4ubkZbZN65UhjooTOinHOzzPG+gD4AkAUDP89XTXPKgB/AJjHOb/XsBECAE7ofT0YxhO6wZrnG7QGHSGkvoiNlQOAixcvIj4+HiUlwvlbI0aMwJgxYyCVSo22S8kcaWyU0Fk5TZI2gzE2D0Ag1Gu4uUA9Du0agJ2c89t1eIv5AJaaKG8DIEnz9RIAGzVf39E874N6Fq4L1Enn+qoNMMY8AYzSvKzNbV1CCKlWUlKSwQxWACgvL8f27dtx9OhRQX0HBwdERESgY8eOJtulZI40BZTQNROc8wzUcMKDme2mmipnjOXqvbzBOT9d5fxyxth3AF4HEM4Ye5hzXvUn51IAcqhvF39b15gJIaQqsVusmZmZiI6Oxr17wpsXHTt2xLRp0+DoaHwVJUrkSFNCCR1pCB8BmAHAB0ASY+xVACkAnKGefatdqPg7zvnZxgmRENIcKZVKxMfHQ6X6e/gw5xynT5/G5s2bUV5eblBfIpEgKCgIQ4cONbp9F6Ce/EDJHGlKKKEj9Y5znsUYmwr1rdlWANaJVNsCdXJHCCEWIXaLtaSkBElJSVAqlYL6bm5uiIyMRLt27Uy2SzNZSVNECZ2VY4y5Qz027WGo92lVwHCR3qo45zyoIWKr8qbHNRM43gAwFUB7qHeHOAf1wshreNWFoAghpAaUSiVSUlKQlye+ec7t27cRHR2NnJwcQVnPnj0RGhoKOzs7kTMBR0dHvP766xaNlxBLooTOijHGnoV60V/t9lqmEjktiyZNmqVJzHlf7QSONzUPQgixGKVSicTERMEtVABQqVQ4fPgwduzYYXDrFVBv3zVp0iT079/f6PZdNFaOWANK6KwUY2wa/p5AoAJwCsBVGO6JSgghLYKxZK6wsBDx8fG4cuWKoKxVq1aYPn06vLy8BGUAoFAosGDBAovHSkh9oITOeml7uc4BiOCc/9WYwRBCSGNJSkoSTeauXbuG2NhYFBYWCsoGDRqECRMmQCaTibYplUoREhJi8VgJqS+U0FmvPlDfPp1LyRwhpKVSKpWCiQ+VlZXYvXs39u3bJ6hvZ2eHsLAwdO/e3WibLi4uCAoKotusxKpQQme9iqGeAPFnYwdCCCGNQWzXh9zcXMTExODmzZuC+u3bt0dkZCRcXV2NtkkzWIm1ooTOep0DMBLqma25jRsKIYQ0HLHlSADg/Pnz2Lhxo+j2XYGBgRg1apTJ7bsomSPWjBI66/UN1Ft9zQKwqJFjIYSQBmFs+66tW7eKJnmOjo6IjIyEv7+/0TZpFitpDiihs1Kc8z8YY+MBvMkYu8k5/6axYyKEkPpWNWnLyMhAdHQ0MjIyBHU7d+6M8PBwODg4CMq0AgICKJkjzQIldFaMc/4cY0wJ4AvG2GtQb6d1F0BlNee90xDxEUKIJYiNleOc4+TJk0hOTkZFRYVBmUQiwfjx4zFkyBCja8spFAqEhIRQMkeaDUrorBhjrC+ARwHIAPgDeNbMUymhI4RYBbFkrqSkBImJiTh37pygvru7O6KiotCmTRujbdJYOdIcUUJnpRhj3QHshnqDe61M0MLChJBmwNjEh1u3biE6Ohq5ubmCst69e2PKlCmwtbU12i4lc6S5ooTOer0DwAVAHoDXAGzgnAtXzySEECuzdu1apKamGhxTqVQ4ePAgdu7cKdi+SyaTYfLkyejbt6/oLVbGGAYOHIjJkyfXa9yENCZK6KzXCPy9sPBvjR0MIYRYglgyV1hYiNjYWFy7dk1Q39vbG1FRUfD09BRtLyAggBI50iJQQme9nDTP2xs1CkIIsQClUon4+HhB79uVK1cQFxeHBw8eCM4ZPHgwxo8fDxsb8V9l/v7+lMyRFoMSOut1DUBPqHeLIIQQq7VixQpkZWUZHKusrMTOnTtx4MABQX2FQoGwsDB069bNaJtSqZTGypEWhRI66/U7gHcBTAGwqpFjIYSQGhObwQoAOTk5iI6Oxu3btwVlHTp0QEREBFxcXIy2K5FIEBYWZtFYCWnqKKGzXp8BiATwPmPsKOf8ZGMHRAgh5jI2i/XPP/9EYmIiSktLDY4zxjBq1CgEBgZCIpEYbZfWlyMtFSV01mswgKUAvgRwkDH2K9Tj6cxZWHhvfQdHCCHGiCVzZWVl2LJlC06eFP5t6uTkhMjISPj5+RltUyaTITQ0lBI50mJRQme9dkM9yxUAGNR7us4y4zwO+ncnhDQCpVKJ5ORkFBcbLpd57949REdHIzMzU3BOly5dEB4eDnt7e9E25XI5pkyZQokcafHoF7t1Y0a+JoSQJkOpVCIxMRHl5eUGxznnOH78OLZu3SrYvksqlWLChAl4+OGHTW7ftWDBgnqLmxBrQgmd9fJv7AAIIaQ6YjNYAaC4uBgbN27EhQsXBGUeHh6IioqCj4+P0XZlMhlCQkIsGish1owSOivFOb/e2DEQQogpa9euFU3mbty4gZiYGOTl5QnK+vXrh5CQEJPbd7m4uCAoKIhusxKihxI6QgghFqVUKhEXFwfOucFxlUqF/fv3Y9euXYIy7Vi4Pn36GG1XKpUiLCyMEjlCRFBC18wwxmyg3kWigHNeUV19QgixJGPLkeTn5yMuLk6wrRcA+Pj4ICoqCh4eHkbbpV45QkyjhK4ZYIxNAPAMgJEAWusdvwtgP4DvOOe0RRghpF4ZS+b++usvxMXFoaioSFA2dOhQBAUFGd2+i/ZiJcQ8lNBZMcaYA4CfAWiXRK86FcwHQBSAKMZYAoCnOOfCDREJIaQOjC1HUlFRgZSUFBw6dEhwjr29PcLDw9GlSxfRNj09PfHSSy/VS7yENEeU0Fm3eABjoU7kMgAkAjgHoACAI9R7vYZC3WsXBiAWQHBjBEoIaZ6USiXi4+OhUqkMjmdnZyMmJgZ37twRnOPn54eIiAg4OzuLtimRSCiZI6SGKKGzUoyxJwAEQb1Q8McAlnDOy0TqyQC8A+BfAMYxxh7nnK9v0GAJIc1WYmKiIJk7e/YsNm3ahLIywx9JjDGMGTMGI0aMMLl9V3h4eH2ESkizRgmd9XoK6mTuO875ImOVOOflABYxxtwBPAf1bhKU0BFC6iQpKQknTpwwmK1aVlaGzZs34/Tp04L6zs7OiIqKgq+vr9E2aR9WQmqPEjrr1V/zvMLM+l9DndD1q5doCCEtxtq1awWzVdPT0xEdHY3s7GxB/W7dumHq1KlGt+8CaPIDIXVFCZ31ctE83zSz/i3Ns6vlQyGEtARKpVJwK5VzjqNHj2Lbtm2orKw0qC+VSjFx4kQEBAQY3b4LACIiIqhXjpA6ooTOeuUAaAX1FmA5ZtTXbhV2v94iIoQ0W0qlErGxsQbHioqKkJCQgEuXLgnqe3p6IioqCt7e3kbbZIxh2rRplMwRYgGU0FmvYwAmA5gP9Xi66szXPAsXiSKEECOUSiUSExNRXl5ucPz69euIiYlBfn6+4Jz+/fsjJCQEcrncaLs0Xo4Qy6KEznr9BGAKgBmMsUIACzjnBVUrMcYcAXwE4AmoJ1H82KBREkKslthCwSqVCnv37sWePXtEt+8KDQ01maTR+nKE1A9K6KwU5zxGs1hwGIDnATzJGNsB4DyAQqjXoesOYBwAB81p8ZzzuMaIlxBiXZRKpSCZy8vLQ2xsLK5fvy6o37ZtW0RGRsLd3d1omzRWjpD6QwmddXsMwEoAs6FO2qZqHvq0I5F/AEB/FhNCqiXWM3fp0iXEx8cLdoMAgGHDhmHs2LFGt++SSqUICwujZI6QekQJnRXjnJcCeJox9jWApwGMANAB6t65QgBpUO/l+gPn/FRjxUkIsR5Vk7mKigps374dR44cEdR1cHDAtGnT0KlTJ6Ptubi4ICgoiJI5QuoZJXTNAOf8JICTjR0HIcS6VV1fLisrC9HR0bh7966g7kMPPYRp06bByclJtC2JRILw8HBK5AhpIJTQEUJIC6dUKhEXF2cwyeH06dNISkoSzG5ljGHs2LEYPny40e27qFeOkIZHCR0hhLRgK1asQFZWlu51aWkpkpKScPbsWUFdV1dXREZGon379qJt0W4PhDQeSuisFGMsEMAuAKkAunDOVSbqSgFchnp83SjO+YGGiZIQ0hQplUokJycLJjjcuXMH0dHRuH9fuP54jx49EBoaCoVCISij26uEND5K6KzXY1DPYP3RVDIHAJzzSsbYdwDe15xHCR0hLZRSqUR8fDxUqr9/bHDOcfjwYWzfvt3gOADY2Nhg4sSJGDhwoOj2XbSuHCFNAyV01msY1AsFbzez/naoE7oR9RYRIaRJE9u+68GDB0hISMDly5cF9b28vDB9+nS0atVKtD1K5ghpOiihs17aQSxXzayvnbrWth5iIYQ0cWJry6WmpiI2NhYFBYJNZjBw4EAEBwcb3b6LxssR0rRQQme9tANZxKeZCWnvlYivMUAIaXaMjZWrrKzEnj17sHfvXsE5tra2mDp1Knr27Gm0XYVCQckcIU0MJXTW6y7Ukxx6AthtRv1emufM+gqIENJ0iI2VA9Tbd8XExODGjRuCc9q1a4fIyEi4ubmZbDskJMSisRJC6o4SOut1EIAfgBdhXkI3F+oxd4frLyRCSFMgtq4cAFy4cAEJCQkoKSkRnDNixAiMGTMGUqnUZNsBAQE0m5WQJogSOuu1BsAMAJGMsbc55+8Yq8gYewvAdKgTujUNEh0hpFGITXwoLy/Htm3bcOzYMUF9R0dHTJs2DR07djTZLi0WTEjTRgmdleKc72CMxQGYBmAJYywSwDoAZwAUQD1Wrg+AJwBofwJv5Jxvbox4CSH1y9gt1szMTERHR+PevXuCczp16oTw8HA4OjoabZcmPxBiHSihs25PAdgAIATqpO1DkTrayRBJUPfoEUKaGbFeOc45Tp8+jc2bNwu275JIJAgKCsLQoUONbt8lk8kQGhpKPXKEWAlK6KwY57wIwGTG2JMAXgYQAMNZr5UAjgH4mnP+ayOESAipZ2LJXElJCTZt2oQ///xTUN/NzQ2RkZFo166d0Tb9/f0xc+ZMi8dKCKk/lNA1A5zzXwD8whhzgnqihBPUt11TOeeFjRkbIaT+rF27FqmpqQbHbt++jejoaOTk5Ajq9+rVC1OmTIGdnZ3RNukWKyHWiRK6ZoRzXgBA2dhxEELqX1JSkkEyp1KpcOjQIaSkpAjG0clkMoSEhKB///6i23cBtOsDIdaOEjorxRjbCfWs1Umc81Iz6ksA7ADAOedB9R0fIaR+iC0WXFhYiPj4eFy5ckVQv3Xr1oiKioKXl5fRNimZI8T6UUJnvUZDndCZXjTqb0zvHEKIFRLbvuvatWuIjY1FYaFwdMWgQYMwYcIEyGQy0fZorBwhzQcldC2H+H0WQkiTZmr7rl27dmH//v2Cc+zs7BAWFobu3bsbbZfGyhHSvFBC13J4a56LGjUKQojZxHrkACAnJwcxMTG4deuWoKx9+/aIjIyEq6uraJu0HAkhzRMldNbP5C1Uph4B7QNgieaQcJANIaTJUSqVosncuXPnsHHjRpSWCofOBgYGYtSoUUa376LdHghpviihsxKMsUqxwwAKjc1aE8EBxFZbixDSaIzdYi0vL8eWLVtw4sQJwTlOTk6IiIiAv7+/aJu2trZYuHBhvcRLCGkaKKGzHsayNnOzORXUu0p8bJlwCCGWpFQqkZiYKNjVAQAyMjIQHR2NjIwMQVnnzp0RHh4OBwcH0XZp4gMhLQMldNbjH1Ve/wh1j9tcAKaWLSkHkA3gNOdcuJkjIaTRKZVKJCQkoLLSsCOec46TJ08iOTkZFRUVBmUSiQTjx4/HkCFDRNeWk8vlWLRoUb3GTQhpOiihsxKc85/0XzPGftR8+YtmCzBCiJVKTk4WJHPFxcVITEzE+fPnBfXd3d0RFRWFNm3aGG1zypQpFo+TENJ0UUJnvfwB3X6uhBArY2ysHADcvHkTMTExyM3NFZT16dMHkydPhq2trdG2AwICaOIDIS0MJXRWinN+vbFjIITUjFKpREpKCvLy8kTLVSoVDh48iJ07d4pu3zV58mT069fPaPtyuRxTpkyhZI6QFogSOkIIaQCmJj0AQEFBAeLi4nDt2jVBmbe3N6KiouDp6Sl6Lq0tRwihhM5KafZyrQ3ay5WQRpCSkmI0mbty5Qri4uLw4MEDQdngwYMxfvx42NiI/7imHR8IIQAldNZsNNSzXKtbtkS78DCr8poQ0oDEbrNWVFRg586dOHjwoKBMoVAgPDwcXbt2NdomJXOEEC1K6KzXWphOzmyg3iFiMAAHANcA7GuAuAgherSTH6q6f/8+YmJicPv2bUFZhw4dEBERARcXF9E2KZEjhFRFCZ2V4pzPNqceY8wJwHsAXgKwinP+WX3GRQj5m7G9WP/8808kJiYKtu9ijGHUqFEIDAyERCIRbZOSOUKIGEromjnOeQGAVxlj7gA+Yowd55zvbuSwCGn2VqxYgaysLINjZWVl2LJlC06ePCmo7+zsjIiICPj5+Rltk5I5QogxlNC1HF8CeALAGwB2N2okhDRjSqUS8fHxgmVH7t27hw0bNgiSPADo2rUrwsLCYG9vL9omzWIlhFSHErqW46rm+eFGjYKQZkzsFivnHMePH8eWLVsEu0FIpVJMmDABDz/8sOj2XQAQERFBiRwhpFqU0LUc3ppnx0aNgpBmau3atUhNTTU4VlxcjISEBFy8eFFQ38PDA1FRUfDx8THapr+/PyVzhBCzUELXcizWPAtXLSWE1IlYMnfjxg3ExMSILlfSr18/hISEVLt9F42XI4SYixI6K8UYCzSjmgJAZwAzoF6+hANYX59xEdLSJCUlGSRzKpUK+/fvx65du8C54cpC2q25+vTpI9qWRCJBeHg49coRQmqMEjrrtRvmLxKsHZyzFwAtW0KIhVQdM5efn4/Y2FikpaUJ6vr4+CAqKgoeHh6ibUmlUoSFhVEyRwipFUrorFt1u0QAQDmA0wB+ArCac15pujohxJikpCScOHFC0PMGAJcvX0Z8fDyKiooEZUOHDkVQUJDR7bv8/f0xc+ZMi8dLCGk5KKGzXv7VlHMAJQDuc84rGiAeQpo1sXXlAPX2XSkpKTh06JCgzN7eHuHh4ejSpYvRdqVSKSVzhJA6o4TOSnHOrzd2DIS0FElJSaLJXHZ2NqKjo5Geni4o8/f3x7Rp0+Ds7Gy0Xe1tVkIIqStK6JoBxpgUgJvmZQ7dViXEcpRKpej2XWfPnsWmTZtQVlZmcJwxhjFjxmDEiBFGt+8CABcXFwQFBdGYOUKIRVBCZ4UYYxIAUQCmARgOoG2V8tsADgCIAxDNOVcJGiGEVEtsoeDS0lJs3rwZZ86cEdR3cXFBZGQkfH19Tba7ZMkSi8ZJCCGU0FkZxth4AN8C0P7GEJsY0Q7AI5rHx4yx5znn2xsoREKaBbGeufT0dERHRyM7O1tQv3v37pg6dSoUCoXJdl1cXCwaJyGEAJTQWRXG2JsAPoQ6idMmcjcAnAeQqznmAqA7gA6a8g4AkhljizjnnzZowIRYCaVSiZSUFOTl5UEqlQq26OKc4+jRo9i2bZvo9l0TJ05EQECA0e27tGQyGYKCgiwePyGEUEJnJRhjTwH4WPOyEMDnAH7gnN80Ur8dgH8AeAOAE4CPGGPpnPNfGiJeQqyFUqlEYmIiysvLAUCQsBUVFSEhIQGXLl0SnOvp6YmoqCh4e3sLyrQYY+Cc05g5Qki9ooTOCjDGXAH8R/PyTwCTOOe3TJ2jKX+XMfYDgM0AegP4gjGWyDkX7kVESAuVkpKiS+aqSktLQ2xsLPLz8wVlAwYMwMSJEyGXy422TTs/EEIaCiV01mE2AA8A2QDGcc4zzD2Rc35bM+7uHAB3TVv/rWkAjDEFgIkAggEMAtARgAPUt3rPAogB8CPnvLiadloDeB3AVKjHARZpYlsDYA0XW7GVkHoktteqSqXC3r17sWfPHsEiwra2tggNDUWvXr1MtqtQKBASEkLJHCGkQVBCZx2mQL1Q8Gc1Sea0OOcZjLHPAXwAIBS1SOgA3IP61m1VngDGah7/ZIxN5Zz/JdYAYywAQBKAVnqHFQACNY9HGGPhnPPSWsRHiFn0x8uJycvLQ2xsLK5fFy712LZtW0RGRsLd3V303ICAAEyePNmi8RJCiDmML5JEmpKumuekOrSxSfPcrZbnOwEoBbAewGNQ99C5A+gHYCXUCWc3ANsYY45VT2aMeQLYCHUylw3gSQA+mnNWaKpNBPBVLeMjpFpJSUmIjY01msxdunQJq1atEk3mhg8fjqefftpoMufp6UnJHCGk0VAPnXXw0jzfrkMbdzTP4juDV28FgHc55/eqHM8B8CJj7DqAjwD4AXgRwCdV6i2EOoFTAZjCOT+sOX4XwMuatfXmAniWMfYV51xZyzgJEWVsgWBAvX3X9u3bceTIEUGZg4MDpk2bhk6dOhlt29HRES+99JLFYiWEkJqiHjrrUKB5rssCVtr9hx7U5mTO+csiyZy+z6HueQOAEP0CxpgMwLOalwl6yZy+JQDKoV56ZU5tYiTElOTkZNHjWVlZ+O6770STuYceeggvvPCCyWQuICAAr7/+usXiJISQ2qAeOutwG+rbm0MApNWyjcGaZ5OzY2uLc17BGPsL6h7ANlWKR+LvZDTayPmZjLE9AMZBPWHi5fqIk7RMSqUSxcXC+TqnT59GUlKSYJarRCLB2LFjMWzYMKPbd2n3YaVJD4SQpoASOuuwA0AfAP8E8FtNT2bq1U5fhXqcW4plQzPQWvNcdY2HgXpfC7tBDMvGAWjPGPPknAt3QyekhpRKJWJjYw2OlZaWIikpCWfPnhXUd3V1RWRkJNq3b2+0TZr8QAhpaiihsw6/AJgPYAhj7FPO+Zs1PP8jqHv3VAB+tnRwAMAY6w/AX/Oy6i3VLppnFQDhaPO/pep93RUAJXSkTj7//HMUFhYaHLtz5w6io6Nx//59Qf0ePXogNDTU6PZd/v7+mDlzZr3ESgghdUEJnRXgnJ9mjP0MYCaA1xhjnQHM45ynmTqPMdYBwBcAwqDunVvHOT9dT2FqtxXjUO81q89T85zDOa8w0Yb+kiy1nbxBCABhMsc5x+HDh7F9+3aoVCqDujY2NggJCcGAAQOMbt9FyRwhpCmjhM56zIW612ow1GvJTdGMOdsD4ALUC/xyAG5QLwUySvOQQD3R4KimDYvT7DGr3aBypcgMVQfNc0k1TekPchIsfUKIOZKSkgSzWR88eID4+Hj89ZdwiUQvLy9Mnz4drVq1EpRp0S1WQkhTRwmdleCcFzPGggGsBvAo1EnaaM3DGG1XwwYAz3POiywdlyamDzUvlVDvHWssjup2gdDvGhGtyxh7HsDzAODr62t+oKTZUyqViIuLE+zskJqaipiYGMGtV0CdqAUHB0Mmk4m26enpScuREEKsAiV0VoRzng/gccbYGgDzoJ5AIDVSvRLATgBfcM631Ec8jLGBUCeLUgA3AUw2svWX9jep+MCkv9npfS26vArn/FtobukGBATQNmEEgPjEh8rKSuzZswd79+4V1Le1tUVYWBh69OhhtM2IiAiawUoIsRqU0FkhzvlWAFs1OzIMgXoyghvUPVz3oV7a5BDnXNglYSGMsS4AkqHeQSITwATO+U0j1bWTG1wZYzYmxtF56X2dbaQOIQbEbrHm5uYiJiYGN28KL8l27dohMjISbm5uRtukZI4QYm0oobNimoRtR0O/L2OsPYDtUCdg+QAmcs4vmjjlsuZZCsAXwDUj9fz1vr5U1zhJ8yaWyAHAhQsXkJCQgJIS4ZDNkSNHYvTo0ZBKxTu2bW1tsXDhQovHSggh9Y0SOlIjjDEvqJM5X6gnMYRyzk9Wc9oJva8Hw3hCp138+AatQUdMWbt2LVJTUw2OlZeXY9u2bTh27JigvqOjIyIiIvDQQw8ZbZNmsRJCrBkldMRsjDFnAFugnm1bDiCKcy4coCS0D0Ae1LtFRAFYL9K2J9SzcgEg0SIBk2ZFqVQiJSUFeXl5grLMzExER0fj3j3h7nSdOnVCeHg4HB3FJ067uLggKCiIbrESQqwaJXTELIwxO6gTrQFQLxD8FOd8sznncs7LGWPfAXgdQDhj7GHO+dEq1ZYCkEN8HTvSgimVSiQnJ4tu3cU5x6lTp5CcnCy6fde4ceMwZMgQo9t30XIkhJDmghI6Ui3GmBTA7wACNYfeAJCkmZQhRiWyRMpHAGYA8NGc+yrU25A5Q72lmXZtiO8458L9mEiLpFQqkZCQgMrKSkFZSUkJNm3ahD///FNQ5ubmhqioKLRt29Zo25TMEUKaE0roiDnaA5iq9/o/mocx1wH46R/gnGcxxqYCSALQCsA6kfO2QJ3cEQIASE5OFk3mbt++jejoaOTk5AjKevXqhSlTpsDOzk5QBtDacoSQ5okSOtJgOOfHGWN9oO7hmwp1olgM4ByAHwGs4VVXhSUtVlJSkuA2q0qlwqFDh5CSkiLYvksmk2HSpEno16+f0e27KJkjhDRXlNCRamn2jBX/DVnztu4BeFPzIETUihUrkJVlONG5sLAQcXFxuHr1qqB+69atERUVBS8vL0GZFt1iJYQ0Z5TQEUKaDGPbd129ehWxsbF48EC4gcigQYMwYcIEo9t3AbRQMCGk+aOEjhDSqEwtR1JZWYldu3Zh//79gjI7OzuEhYWhe/fuRttmjGHatGmUzBFCmj1K6AghjUapVCIxMVGw5AgA5OTkICYmBrdu3RKU+fr6IiIiAq6urkbbVigUCAkJoWSOENIiUEJHCGkUSqUSsbGxomXnzp3Dxo0bUVpaKigbNWoUAgMDjW7fBdAtVkJIy0MJHSGkwRnbh7W8vBxbtmzBiRMnBGVOTk6IiIiAv7+/oEwfJXOEkJaIEjpCSIMxtetDRkYGNmzYgMzMTEFZly5dEBYWBgcHB6Nt03g5QkhLRgkdIaRBGBsvxznHiRMnsGXLFlRUVBiUSSQSTJgwAYMHDza6thygXoMuNDSUkjlCSItFCR0hpF6ZmsVaXFyMxMREnD9/XlDm7u6OqKgotGnTxmjb1CtHCCFqlNARQuqNqVmsN2/eRHR0tGii16dPH0yePBm2trZG25ZIJAgPD6dkjhBCQAkdIaQemOqVU6lUOHDgAHbu3ClYQFgmk2Hy5Mno16+fyfbpFishhBiihI4QYlGmeuUKCgoQFxeHa9euCcq8vb0RFRUFT09Po21LpVKEhYVRIkcIIVVQQkcIsajk5GTRZO7KlSuIi4sT3b5r8ODBGD9+PGxsxH8kMcbw9ttvWzxWQghpLiihI4RYzIoVKwRLklRUVGDnzp04ePCgoL5CoUB4eDi6du1qtE3txAdCCCHGUUJHCKkzY7s+3L9/H9HR0bhz546grEOHDoiMjISzs7PRdmmsHCGEmIcSOkJInRjb9UGpVGLTpk2C7bsYY7rtuyQSiWibLi4uCAoKokSOEELMRAkdIaRWlEolEhISUFlZaXC8rKwMycnJOHXqlOAcZ2dnREZGokOHDkbbXbJkicVjJYSQ5o4SOkJIjRm7xXr37l1ER0cjKytLUNa1a1eEhYXB3t7eaLsREREWjZMQQloKSugIITUidouVc45jx45h69atgh47qVSK4OBgDBo0yOT2XREREXSLlRBCaokSOkKI2VasWCHofSsqKsLGjRtx8eJFQX0PDw9ERUXBx8fHZLuUzBFCSN1QQkcIMUmpVCI5OVmwHAkAXL9+HTExMcjPzxeU9evXD5MmTYJcLjfatkKhQEhICCVzhBBSR5TQEUKMMjbxQaVSYd++fdi9e7dg+y65XI4pU6agT58+RtsNCAjA5MmT6yVmQghpiSihI4SIUiqViIuLEyRs+fn5iI2NRVpamuAcHx8fREVFwcPDw2i7lMwRQojlUUJHCBEwtrbc5cuXER8fj6KiIkHZ0KFDERQUZHT7LoDGyhFCSH2hhI4QoqNdDLisrMzgeEVFBXbs2IHDhw8LzrG3t8e0adPQuXNno+3SWDlCCKlflNARQgAY75XLzs5GdHQ00tPTBWX+/v6IiIiAk5OT0XapV44QQuofJXSEtGCmZrACwJkzZ5CUlCTosWOMYcyYMRgxYoTR7bsA9Xg5SuYIIaT+UUJHSAtlbLcHACgtLcXmzZtx5swZQZmLiwsiIyPh6+trtG2pVIqwsDBK5gghpIFQQkdIC5WQkCB6PD09HdHR0cjOzhaUde/eHVOnToVCoTDaLt1iJYSQhkcJHSEtzNq1a5Gamio4zjnHkSNHsH37dtHtuyZOnIiAgADavosQQpogSugIaQGUSiVSUlKQl5cnWl5UVIT4+HhcvnxZUObp6Ynp06ejdevWJt+DkjlCCGk8lNAR0swplUokJiaivLxctDwtLQ0xMTEoKCgQlA0YMAATJ040uX0XQMkcIYQ0NkroCGnmUlJSRJO5yspK7N27F3v37hXsBmFra4vQ0FD06tWr2vZpJishhDQ+SugIaebEbrPm5eUhNjYW169fF5S1bdsWUVFRcHNzq7ZthUJB23gRQkgTQAkdIc2ci4uLQVJ38eJFJCQkiK49N3z4cIwdOxZSqbTadmUyGUJCQiwaKyGEkNqhhI6QZi4oKAixsbEoLy/H9u3bcfToUUEdBwcHTJs2DZ06dTKrTRcXFwQFBdGtVkIIaSIooSOkmevduze+/fZbREdH4+7du4Lyhx56CNOmTTO5fRdASRwhhDRllNAR0oxxzrF27Vp8++23gu27JBIJxo4di2HDhhndvisgIIDGyBFCiBWghI6QZqqgoABz587FunXrBGWurq6IiopCu3btDI5LJBKEh4dTLxwhhFgZSugIaYZOnDiBxx57DFeuXBGU9ezZE1OmTDHYvosxhoEDB1JvHCGEWClK6AhpRlQqFb788kssXLhQsPacQqHAggUL4OLigvz8fBoTRwghzQgldIQ0E5mZmZg9ezY2b94sKOvVqxd+//139OjRoxEiI4QQUt/ER0ITQqzKzp070bdvX9Fk7oUXXsDRo0cpmSOEkGaMEjpCrFhFRQX+7//+D+PGjUN6erpBmaurK6Kjo7Fy5UqD8XKEEEKaH7rlSoiVunHjBmbMmIEDBw4IyoYNG4Zff/0VHTp0aITICCGENDTqoSPECsXGxqJv376CZI4xhrfeegt79uyhZI4QQloQ6qEjxIoUFxfj9ddfx8qVKwVl3t7e+OWXXxAUFNQIkRFCCGlMlNARYiUuXLiARx99FEqlUlAWEhKCNWvWoFWrVo0QGSGEkMZGt1wJaeI45/j+++8xcOBAQTInk8nw2WefYdOmTZTMEUJIC0Y9dIQ0YXl5eZgzZw5+//13QdlDDz2E3377DYMGDWqEyAghhDQl1ENHSBN19OhR9O/fXzSZmzFjBk6dOkXJHCGEEACU0BHS5KhUKnz66acYPnw4UlNTDcrs7e3x448/4pdffoGzs3MjRUgIIaSpoVuuhDQh9+7dw6xZs7B161ZBWd++ffHbb7+hW7dujRAZIYSQpox66AhpIrZv346+ffuKJnMvv/wyDh8+TMkcIYQQUZTQEdLIysvLsWjRIgQHB+PevXsGZW5uboiPj8dXX30FOzu7RoqQEEJIU0e3XAlpRKmpqZgxYwYOHz4sKBs5ciTWrVuH9u3bN0JkhBBCrAn10BHSSDZs2ID+/fsLkjnGGN5++23s3LmTkjlCCCFmoR46QhpYUVER5s2bh//973+CsjZt2mDdunUYPXp0wwdGCCHEalFCR0gD+vPPP/Hoo4/i/PnzgrIpU6bgxx9/hKenZyNERgghxJrRLVdCGgDnHKtXr8agQYMEyZxcLseXX36JjRs3UjJHCCGkVhjnXPdi2bJl7SQSSZhCoXhcpVJ1UKlUto0YGyEmlZaWerm5uTV2GNXinKOgoAClpaWCMqlUCmdnZ9jYUGd5UyaTyeDq6orOnTuja9eucHJyauyQCCEtEGPsBOc8QLRMm9AtW7ZsvL29/c89evSQdu/enXl5eVXI5XIudhIhTUF2drZ3U+/RKisrQ25uLiorKwVlCoUCzs7OkEioo7ypKysrQ1ZWFi5duoSrV69i0qRJ8PPza+ywCCEtjKmEzgYAli1bNtTR0fGXxx9/nLVp06a8YcMjpHZkMhnkcnljhyGKc47CwkIUFBRAIpEYJG2MMbi4uMDe3r4RIyQ1IZfL4ejoCD8/P9y5cwcJCQkICwtDmzZtGjs0QggBoBlDZ29v/8a4ceNkbdq0qWjsgAixdpWVlcjOzkZBQYGgTCaTwcvLi5I5K9amTRsMGzYMJ0+ebOxQCCFER7Js2TInzvnIrl27Cgf4EEJqpKSkBJmZmSgrKxOUOTg4wNPTk8bLNQOdO3dGWloaysvphgYhpGmQAOju7u5ebmdnR+PlCKklzjny8vJw//59qFQqgzKJRAJ3d3e4uLiAMdZIERJLsre3h4uLC7Kzsxs7FEIIAaAeQ+esUCgaOw5CrFZFRQVycnJEe2vkcjnc3NwglUobITJSn+zs7ERnLhNCSGOwAcCo14CQ2ikqKkJeXh70l//RcnJygqOjI/XKNVOMMdF/d0IIaQw0mIeQWlCpVMjPz0dRUZGgTCqVwtXVFba2tIwjIYSQhtHsF8C6evWqlDHmM2PGDNfGjqWhtMTvuSHMnDkTrVq1Qm5uLrKyskSTOTs7O3h5eQmSubS0NDDGMHv27AaKtvZOnDgBxhi+//77xg6FEEKImcxO6BhjPvoPW1tbHw8Pj9Z9+vTxnDlzpktCQoJtRUXdVj05fPiwjDHmExAQILpa7Jo1axTa9//rr78Eg5KKiopgZ2fno1AovEtKSuoUS1NnjUmbNcasdfz4cfzyyy947bXXUFRUBLFr3cXFBW5ubla/UPDAgQMRHh6O//u//0NhYWFjhwMAyM7OxnfffYdp06ahU6dOUCgUcHFxwYgRI/D9998LJqKY69atW3j66afRpk0b2Nraws/PD/PmzUNOTo6FvwNCCKlfNb7lunDhwkJAvdZWbm4uu3Dhguz333+3//nnn+379etXvm7dupwePXoIl8U3w6BBg8pdXV356dOnZbm5uczV1dVggMrOnTvl2nEr27dvt+3cubNBF8nu3bvlpaWlGD16dJmdnV1tQmgW2rdvX6lUKjNdXV1r91uOCCxatAhOTk6YPn26oMzGxgZubm6QyWSNEFn9WLRoEQYPHozly5dj8eLFjR0ONmzYgLlz58LHxwdjxoyBr68v7t27h9jYWDz77LNITk7Ghg0bajRe8erVqxg2bBgyMjIQFhaGbt264ejRo/jvf/+LLVu24MCBA/Dw8KjH74oQQiynxl0JH374YcGHH35Y8MknnxR8++23+fv27ctOTU29FxYWVnL69GnZhAkTPNLT02vVRSGVSjF8+PDSyspKpKSkCLYA2LNnj+3w4cPL3N3dVbt27RKUp6Sk2ALAmDFjhIuAtSByuRy9evWqaNeuHSV0FvDnn38iJSUFoaGhqDoj3N7eHp6ens0qmQOAhx9+GN26dcPq1atFty1raF26dMHGjRtx69YtrFu3Dh9++CF++OEHXLx4Ee3bt0dMTAxiY2Nr1OaLL76IjIwMLF++HPHx8fjoo4+wc+dOzJ8/H5cuXcJbb71VT98NIYRYnkXuDbVp00YVExOTM3z48LLbt29L33nnHceqdX755Re7oUOHejg7O3srFAqfHj16eC1dutSx6q3RsWPHlgJ/J2daV69eld64cUM6ZsyY0mHDhpXt27dPMOJ8z549tgAwfvx40bUErl69Ko2IiHDz8PBobWdn59OvXz/P2NhYg3b+/PNPG8aYz8iRI43+ad6jRw8vmUzmc/v2bbM+v/3798umTp3q1qpVq9Zyudynbdu2rf/xj3+43Lx5U3B+dHS0bWBgoEfr1q1b29ra+nh7e7ceNmyYxxdffKHbWmDRokVOnTp1agUA69evV+jfCl+9erXC2K1N/eOXLl2STp061c3d3d3b0dHRe8yYMe6nT5+2AYC7d+9KZs+e7dK6devWdnZ2Pv379/fcunWrIIFevXq1IjQ01K1Dhw6tFAqFj5OTk/fgwYM9v//+e8E6ONXFXJvPylz79+/H6NGj4ezsDBsbG3To0AHvvfdetbfpOOcoKCjAqlWrwDlHaGiorowxBldXV7i6umLTpk0ICgqCj48PbG1t0aZNG4waNQrffPON2TH+8ccfCAwMhIuLCxQKBXr37o0PP/xQsCyG/li8ixcvIjw8HO7u7nBwcMCIESOwbds2o+9x5MgRREVFwdvbG3K5HO3bt8ecOXNw584d0fqPPfYYbty4gR07dpj9fdSXsWPHIjQ0VHA729vbGy+88AIAYPfu3Wa3d+3aNWzbtg1+fn546aWXDMqWLVsGBwcH/Pzzz3jw4EGdYyeEkIZgscE+UqkUb731VgEAREdHK/R/Wb7++utOTz31lNvly5dtpk+fXvzcc8894Jxj2bJlTkFBQR76q+qPHz++DAB2795tkGht3brVFgDGjRtXOnr06NJ79+5Jzp49q7tlnJuby06fPi1zcXHhDz/8sGBBsBs3bkgHDx7seePGDemjjz5aPG3atOKLFy/Kpk+f7q6frPTq1atixIgRZfv375efP39eME5vz549sgsXLtiEhISUtG3bttoesFWrVilGjx7tuWPHDtuRI0eWzp0790G/fv3K1q5daz9o0CCv1NRU3Xt89dVX9tOnT3e/dOmSTUhISMnLL79cOGHChJKSkhK2du1aXUI3ZsyY0hdeeOEBAPTs2bNi4cKFhdrHgAEDql26/vr169Jhw4Z5ZmRkSGbMmFE0ZsyY0j179tgGBQV5XLhwQTpkyBDPEydOyCMiIorDw8OLz507JwsLC/PQjxUA5s2b53rjxg3p8OHDy+bOnVsYGRlZfPPmTemzzz7rumDBAif9uubEXJPPylz/93//h8DAQOTm5uKFF17Ac889h/z8fPz73//G+++/b/Q8/e279u3bB6lUioEDBwIw3L7r22+/RVhYGM6fP4/Q0FC8/vrrmDRpEoqLi/Hjjz+aFePixYvx6KOP4sKFC5gxYwZefvllcM6xePFiBAcHi65vl5qaiqFDhyI7Oxtz5szB9OnTceLECYSEhOD3338X1P/xxx8xfPhwJCcnY8yYMZg3bx4CAgLw3XffISAgADdu3BCcM3z4cADA9u3bzfo+Gou2d7QmO3Ds3LkTADBhwgRBkujk5IThw4ejqKgIhw8ftlyghBBSn5YuXTp+3bp1tznnd0w9AHAA3FSd4uLiOzY2NhwAv3z58j3O+Z09e/ZkAuBt27atuHXr1l1t3bKysjvBwcElAPjbb7+dr9+Ot7d3JWOMp6en6+pHRUUVOTg4qMrKyu6cPn06AwD/7LPPcrXl0dHR2QD45MmTi/XbunLlyj1t7IsWLTJ4n40bN2YD4OPGjSvRP75u3br7APjLL79cWPV7fPzxx4sA8E2bNmVX95mdO3funkwm435+fhXXr1+/q1+WnJycJZFI+KRJk3Tx9unTp0wul/M7d+7crdrWvXv37op9X48//nhR1brGyvQ/i3//+98Gn8Vbb72VD4C7urqq/vGPfzyoqKjQlf3vf//LAcDnzp1r8HlcvHjxXtX3LikpuTNy5MhSGxsbXvV7NhVzTT8rzvmdjIwMbsp//vMfDoAvWLCAq1Qq3fFTp05xzffKKysrBecVFxfz9PR0fvv2bf7XX39xqVTKu3Xrxm/fvs1zc3MN2howYACXy+X83r17gnYyMzN1X6empnIAfNasWQZ1Dh48yAHw9u3b8/T0dN3x8vJyPmXKFA6Av//++4J2APA33njDoK1jx45xGxsb7urqyvPy8nTHL126xGUyGe/YsSO/deuWwTkpKSlcIpHw8PBwQfy5ubkcAB80aJCgzJgvvviCL1myxOxHXFyc2W2LKS8v57169eIA+JYtW8w+74033uCanyGi5S+99BIHwL/55hujbWzYsIGnpqbWNGRCCKk1AMc55xB7WDSh45zf8fLyqgTA9+/fn8k5vzNz5swHAPjy5ctzq9Y9d+7cPYlEwn19fSv0j0+fPr0IAP/555/va4+1bt26Uj/x8vT0rNRP3ubOnVsIgH/++ecG76NNItq3b19RXl4uiLddu3YV7u7ulfrHysrK7nh7e1e6u7tXFhcX645nZ2en29nZqfz8/CoqKytNfg76McXGxoomf5MmTSqWSqU8Nzc3nWsSOoVCocrKykqvru26JHRin8W1a9fuAeAKhUKljUf7KC8vv2NjY8OHDx9eWl1cnPM769evvw+Af/vttznmxlzTz4pXk9Ddvn2bOzg48KFDh4ombd26deMAeFpamu6YSqXiubm5/Pbt27rH3r17OQAeGBjIi4uLBe0MGDCA29vb8/v37xuNhXPjCd2zzz7LAfDVq1cLzrl06RKXSCTc399f0I6LiwvPz88XnDNr1iwOgK9Zs0Z3bN68eVzzR4hobOHh4VwqlYq2Z2dnx1u3bm3ye9PXoUMHXcJpzqPq51FTr7/+OgfAJ02aVKPznnvuOQ6A/+9//xMtX7x4MQfAP/jgA6NtUEJHCGlophI6iy8szDUrp2tnm50+fVoGiI9r69GjR2WbNm0qb9y4Ib1//z5zd3fngHoc3YYNGxQ7d+60ffLJJ0uUSqXNvXv3JC+//LKujREjRpTt3r3btrKyElKpVDd+Ljg4WHT8XK9evcrFbsm0bdu28tixYwbjw2QyGWbNmlX08ccfO/7222+K2bNnFwPADz/8YF9SUsKefvrpB+YsTXH06FE5oL59rP1aX1ZWlrSyshIXLlywGTJkSPljjz1WvHjxYueePXu2ioiIKB49enRZYGBgmbe3t0UnN/Tu3VvwWbRr164SADp27Fjp4uJiMLvYxsYGXl5eqjt37hjc8kxNTZV+8MEHjrt375bfunVLWlJSYjDF8Pbt22bfIq3pZ1Vde+vXr8eDBw/w2muviS4j4uDgAAC6LbmMbd+lXb6idevWEJs5/cQTT+D1119Hz5498eijj2LUqFEYPnw4vLy8qv+mAZw8eRKAeoxYVV26dEG7du2QmpqK3NxcuLq66soGDBgAJycnwTmjR4/GTz/9hFOnTmHWrFkAgEOHDgEA9uzZg2PHjgnOycjIQGVlJS5fvqy7razl7u6Oe/fumfW9AOoxfg1l+fLl+Pzzz9GtWzf8/PPPFm276s8xQghp6iya0BUXFyM3N1cCAK1bt1YBQH5+vgQAjI03a926terWrVvS3Nxcibu7eyUABAcHG4yj27ZtmxwAxo0bpxtsN2rUqNL4+Hi7Y8eOyR566KHKCxcu2Pj4+Kh69uwpOiWv6hIoWlKpVHRw/Isvvvjg888/d/z222/t9RM6uVyO5557rticz+P+/fsSAFi+fLmDqXqFhYUMABYtWvTA09NTtXr1aofVq1c7rFy50oExhmHDhpV9+umn+UOHDq02kTFH1YQN+HsckrOzs+i/k42NDddfe+3y5cvSoUOHeubm5kqGDBlSNnbs2FIXFxculUpx/fp16fr16xWlpaVm/zas6WdVnYSEBEgkEkyaNEm0/Pbt23BwcECbNm1Mbt+lXbbC2J6dr732Gjw9PfHNN99g+fLl+PLLL8EYw6hRo/Dpp58iICDAZJx5eXkAAB8fH9FyHx8f3LhxA3l5eQYJXevWrUXre3t7G7QLQLeB/KeffmoyFrE154qLiwUze5uCFStW4NVXX0WPHj2QkpICd3f3Gp3v4uICwPBz0pefn29QjxBCmjqLJnS7du2SV1RUwMvLS9WxY8dKQJcgSO/cuSPp2rWrINm6d++eBAD010zz9/ev9PPzq0xNTZWmpaVJdu3aZevi4sIHDRqkS2i0yd2OHTvkfn5+lZxzjBo1ymI7Zfv6+qqCg4NLkpKS7JRKpc39+/fZhQsXbKZNm1Zibo+Z9nvPycm5ayyhrOq5554rfu6554rv37/P9u7dK4+Li7P75Zdf7CdPnuxx7ty5DB8fnyaxFMmnn37qeP/+fcmqVaty58yZY5Dg/vTTT4r169fXKAuozWdlTEVFBY4ePQpPT0/Y29sLyo8dO4a7d+9i8uTJyMvLQ3GxMD+XSqVwc3PTvdYmRWJmzpyJmTNnIjc3FwcPHkRcXBx++OEHBAcH48KFC2jVqpXRc7UJw927d9GxY0dBeXp6ukE9LWO9Znfv3hXU109enJ2djcZSlUqlQm5uLvz9/c0+58svv0Rubq7Z9fv164fw8HCz62vfY/78+ejVqxdSUlJMfr7GdO3aFQBw+fJl0fK//voLgLqXlBBCrIHFErrKykp88MEHTgAwffp03W/Ivn37lp89e1aWkpJi27VrV4OFgC9evCi9c+eO1NfXt1J7u1Vr9OjRpWvWrLHfsmWL7cGDB22HDRtWqr09Bqhno7Zu3Vq1e/duW19f30rg7yVPLOXFF198kJSUZLdy5Up7bc/jnDlzzF7HYNCgQeVnzpyR7dq1Sz5t2rQaxebu7s7Dw8NLw8PDS1UqFX755Rf73bt3yx9//PESAJBKpRxAo60Rdu3aNSkAPPbYY4ItOfbs2SO4ZQqYjrkun1VV586dQ2lpKcrKyqBSqQS3XD///HMAwKOPPiqazNnZ2cHV1RUSiQQ+Pj7w8vLCpUuXqn1fV1dXTJo0CZMmTYJKpcIPP/yAffv2ITIy0ug5/fv3x8mTJ7F7925BQnflyhXcunUL/v7+Br1zgPpWbUFBgeC2q3bpjv79++uODRkyBCdOnMC+ffswefLkar8PrUuXLoFzjn79+pl9zpdffonr16+bXX/WrFk1Sug+/vhjLFy4EP369cP27dvh6Sm6qUy1xowZAwDYtm2b4BopKCjAgQMHoFAoMGTIkFq1TwghDc0iy5akp6dLIiMj3Q4cOCBv165d5ZIlS3T3bp555pkiAPjoo48c7969q3u/iooKvPbaa84qlQqzZs0SbIqpTc6WL1/umJOTw0aPHi1YLHjEiBGlhw4dkmvXrDM2fq62Jk6cWNaxY8fK9evX28fFxdl17NixUns72ByvvvrqA5lMhjfeeMNFbAmU0tJS7Ny5U5f8bNy40VZsiYrMzEwpANjb2+uSXnd3d84Yw82bN2u8lIcldOjQoRJQ95DqH09MTLTVX2JFn6mYa/pZmXLixAkAQG5uLrZs2WJQ9vXXX+P333/HmDFjEBQUZFDGGBNs38UYQ2BgILKysnDlyhXBe23ZskV0G7CMjAwAEO0h1Pf0008DAN577z1kZmbqjldWVuKNN96ASqXCM888IzgvLy8P77zzjsGx48ePY926dXBxccG0adN0x19++WXIZDLMnz9ftEeqrKwM+/btExzXLtmhTX7MkZaWJjpY19hjzZo1Zrf97rvvYuHChRg4cCBSUlLMSuauXr2KixcvCsZGduzYERMmTEBaWhpWrFhhULZkyRI8ePAAM2fO1I21JISQpq7GPXSLFi1yAnS3Y9iFCxdkR44ckZeVlWHAgAHl69aty2nVqpXutuCoUaPKX3311cL//ve/jr179/YKDw8vsbe359u3b7e9cOGCzeDBg8veeustweCd8ePHlzHGcOHCBRvNa0GyNnr06LKYmBjFjRs3pJ06darw9fW16O1IiUSCZ5999sGiRYucAeCZZ56p0SqjvXr1qli5cmXu3LlzXfv27dsqKCiotFOnThXl5eW4efOm9PDhw7YeHh6Vf/31VyYAPPXUU262trZ8yJAhZR06dKjknOPgwYPyU6dOyfr27VseEhKi+wycnZ35wIEDyw8fPiyfPn26a+fOnSukUimmTZtWIjZGztJefvnlB+vWrbN/4okn3H/77bfiNm3aqM6fP2+TkpJiGx4eXhIXFyeYQWAq5gEDBtToszJFm9CFhoYiKioKM2bMgJeXF/bu3YuDBw+ib9+++Prrrw3OMbV9V2RkJGJiYrB161Z06tTJoOyxxx6DnZ0dRowYAT8/P3DOsW/fPhw7dgwDBw7EuHHjTMY6bNgwLFiwAJ988gl69eqFqKgoODg4IDk5GX/++SdGjBiBN998U3BeYGAgvvvuOxw5cgTDhw9Heno6fv/9d6hUKqxevdrg1mq3bt3www8/4Omnn0bPnj0xceJEdOnSBeXl5bhx4wb27dsHLy8vXLx40eA9tm3bBqlUirCwMNMfeAP46aef8Pbbb0MqlWLkyJFYvny5oI6fnx9mz55tcCwoKAjXr19Hamoq/Pz8DMq++eYbDBs2DK+88gpSUlLQvXt3HDlyBLt27UKXLl1MrlNICCFNTk2XLdE+5HI5d3d3r+zTp0/ZzJkzHyQkJGTrr11W9fHTTz/dHzx4cKmDg4PK1taWd+3atfzf//53flFRkdFzevToUQ6Au7u7V4otE3LhwgXdumrPPvvsA7E2TC2VwTm/M2zYsFKYWI4lKysrXSKRcFtbW56RkVHtciJij5MnT2Y8/vjjRe3atauQy+Xc1dVV1a1bt/LZs2c/SE5OztLW+/LLL3MnTZpU7OvrW2FnZ6dydXVV9erVq/ydd97Jq7qUiPb7Dw4OLnFzc1MxxjgAvmrVqpzqli0x9lkA4MOGDRNdmqR9+/YV7du3N1heZvfu3ZkjRowodXFxUTk4OKgGDx5c+vvvv9/funVrFgC+cOHCAnNjrulnxU0sWzJ48GAul8v5gwcP+MKFC3mbNm24XC7nHTt25AsWLOBXr141WJokJydHdGkTrdLSUt66dWv+8MMPC8pWrlzJw8PDub+/P1coFNzNzY3369ePf/zxxwbLgBhbtkRr/fr1fPjw4dzR0ZHb2tryHj168Pfee0+wVIp+O+fPn+dTp07lrq6uXKFQ8GHDhplci+3s2bN81qxZ3NfXl8vlcu7m5sZ79uzJn3/+eZ6SkmJQNzc3l9vZ2fGwsDCj7TWkJUuWVLsEyqhRowTnaZdRMba8yI0bN/js2bO5t7c3l8lk3NfXl7/yyis8Ozu72pho2RJCSEODiWVL2NKlS8d37tx5zYwZM2h+voht27bJg4ODPR555JHi33//Pbex4yF/y8zM9Km6PEhlZSWcnJzQvXt3nDhxQrd9l9gMTu32XebM4vzwww+xePFinDx50mB8WkNLS0uDv78/Zs2aVaPblTX11Vdf4ZVXXsHevXsxcuTIensfaxYdHY2AgABBzx8hhNQXxtgJzrno8gkW2/qrufr0008dAeCf//wnbepoIcXFxXZZWVnud+/ebX3nzh2fe/futcrNzXWpqKio83jA8+fPo7i4GAMGDNBt3yWWzGm37zJ3SY758+fD19cXb7/9dl1DbPKKi4vx4YcfIjIykpI5QgixEhZfWLg5OHXqlE1CQoLdyZMnZTt27LANDg4uHTFihEXWgGvJOOfIzc11KS4uNpgpUFlZKS0qKrIvLi5WuLq65igUilpPbtGOn+vVqxcyMzNF1xh0dHSEk5NTjRaNtbOzw88//4xdu3bhwYMHzXqwfFpaGp5//nnBeDRCCCFNFyV0Io4ePSpbtmyZk5OTEw8LCyv59ttvxVcfJTVSUFDgqE3m7OzsShwdHQukUqmqrKxMlp+f71JZWSnNzc11s7GxyZTJZLVaj+X48eMAgIceekiQzEkkEri5ucHW1rZW8QcGBiIwMLBW51qT7t27Y+nSpY0dBiGEkBqghE7EnDlziqsulkvqprKyUvLgwQNHALC1tS11c3PL0faQKRSKUplMlp2ZmenFOWcFBQXO7u7uOTV9j4qKCrz99ttYvHixoMzW1haurq7QX8vQGmln0hJCCCH6bADQLwhS74qKihSccwYATk5O+VVvd9rY2FQqFIqioqIih5KSErvKykqJVCo1axkazjmKi4uNbt/l7OwMBwcH2peTWBTnnK4pQkiTIQFQUFIiWOyfEIsqKSmxAwCpVFopl8uFK/ECUCgUxXr1zbovqt2eKjc3V5DMSaVSeHp6wtHRkX7xEosrLS2t9e17QgixNBsAl7Kzs+VlZWUVcrmcuupIvaioqJABgEwmM7rThlwu1008KS8vlwEwedubc47MzEzRrcT0t+8ixNJKS0uRk5NjsN8vIYQ0JsmSJUtyGGNHr1y5Yta2SoTUVEVFhUR7u9XGxsboZAfGGCQSiUpzTrXjOysqKgTJnNj2XYRY2pUrV9C+fXvqoSOENBkSACgqKvpy27ZtqqysLOseMU6aJJVKpcusJBKJydmr2nL9c8xlY2MDT09PGi9H6lVWVhb279+Pfv36NXYohBCiIwGAJUuWbC8oKPjn2rVrsWfPHrt79+7ZiK3fRUhtaHvnAFSbaGm2AwPnvEYJnb29Pby8vET3YiWkrlQqFTIyMrB//35ER0cjMDCQdogghDQpTH8g+bJly/rIZLJHZTLZ9IqKilaaWYY0ro7UiUqlsikpKXEGALlc/sDGxsbowsElJSXOKpXKRiKRqOzs7HKrlldUVNhWVFTYadqVVtfjR4gYzjnT/vFgZn2UlJSUK5XKnJMnT95PT0+nmWQtmyeArMYOglilul47HTjnXmIFzNiSJcuWLZMCsKvDmxICAFi9enWvrKyswwBga2v7+htvvLHaWN0PPvhgP+e8H2PszOLFi4ebaveDDz7Yt3jxYtqbitRYLa6dkiVLltAfDwQAwBg7bmw/TUJMqc9rx2hCR4ilMMbaArilefkO53yJibo3ALQHsINzPr6adumHKqkVunZIXdD1Q2qrPq8dmgZI6h3n/DaAQs1Lf2P1GGO2ANpoXl6s77gIIYSQ5oISOtJQTmmeB5uoMxCAdqb1STPa/LZOEZGWjK4dUhd0/ZDaqrdrh265kgbBGHsTwCeal30450qROv8BMB+ACkAbzvm9BgyREEIIsVrUQ0cayloARZqv369ayBjzA/C85mUsJXOEEEKI+SihIw1Ck6BpE7lQxthvjLHejDEvxtgUADsBOEA91u4tY+0wxsIZY1sYY/cYYyWMsWuMsW8YYx3r/7sgTRFT684Ym8UYW8EYO8YYK2WMccaY2cuLMMZaM8Y+YYxdZIwVMcayGGN7GGP/YLRSdbPEGFMwxqYxxlYxxk4wxnIZY+WMsUzGWApj7EXGmMKMdujaaWEYY+0ZYy8zxn5ijJ1kjN3W/NwpYIwpGWPLGWPdzGzLItcP3XIlDUZzYX4H4GkjVQoBPMo532zJc0nzpundTTVSXMo5r3b5JcZYAIAkAK2MVNkCIJxzbnQNRWJ9GGP5AJyqqXYRwFTO+V9G2qBrpwVijL0M4KtqqpUDeI1z/rWJdix2/VAPHWkwXO0ZABEAtgLIBFAK9S/jVQD6mUjIFuHvZO4PAH2h/g8QBiANgCOA3xljXertGyDW4DaAOAD7zD2BMeYJYCPU11M2gCcB+ADoBmCFptpEVP/Dm1gfJ6h/Bq0H8BiAjgDcAfQDsBLqhfW7AdjGGHOsejJdOy1aMdSJ2OsAxkL9b+4JoCeAZwFcASAD8BVjbLJYAxa/fjjn9KBHk34AaA11DxwHsAmanmW9cn+98g2NHS89Gvz6cII6sffWO7ZUcz2UmHH+Z5q6lQCGiJR/oylXAejd2N8vPSx67XwNoLWJ8n9p/u05gAUi5XTt0MPYteMK9R+YHMBuI3Usev1QDx2xBjOhHl8HAG9xzZWuxTlPBfA/zcsIxljrhgyONC7OeQHnPIFzfrem5zLGZFD/NQ0ACZzzwyLVlkB964QBmFP7SElTwzl/mZuegPU51D0nABCiX0DXDjGFc54LIEbzcmDV8vq4fiihI9YgVPP8F+f8jJE60ZpnCYBJ9R8SaSZGAnDRfB0tVoFznglgj+bl1IYIijQNnPMKANqxc22qFNO1Q6pTrnkWm5xl8euHEjpiDfprno+YqHMc6m5rABhQv+GQZkT/L2dT15e2rL1m3AtpObQ9/vlVjtO1Q4xijNnh7yTsuEgVi18/lNCRJk2zD6x2MPI1Y/W4egZQuualWVPFCQGgnUSjAnDdRD39WbRd6y8c0pQwxvrj7+0Kq94So2uHGGCMSRhjPoyxqQD2AugEoAzqW6dVWfz6salBrIQ0Bv2/SDKrqZsBoB0Aj/oLhzQz2usrR3N7zZgMva/p+mo5PtU8cwi3bKJrhwAAGGNbAASLFF0CMIdzflSkzOLXD/XQkabOQe/r6haJLdY8C5YXIMQI7fVl7rUF0PXVImi2KwzSvFzJhdsV0rVDTMkE8CWAE0bKLX79UEJHmjr9VbKrWwVbW5dWyybmMveaqcl1SKwcYywYwIeal0oAb4hV0zzTtUPCoV4+yQXAQ1CvzJAF9VqGRxhj/iLnWPz6oVuupKkr1Pu6ui14tDsCPKinWEjzo72+zL22ALq+mjXG2EAAGwBIAdwEMJlzXixSla4dAgDgnOv3suUDSGWMRQPYBWAwgI2Msb6cc5VePYtfP9RDR5q6LL2vvaqpqy3PNlmLkL9pry9XxpipP3D1rz26vpopzU4zyVD3tmQCmMA5v2mkOl07xCjNHwELNS97Qb2bhD6LXz+U0JEmjXN+G3//JSPWbQ0AYIzZ4u91oi7Wd1yk2biseZYC8DVRT//au1R/4ZDGwhhrD2A71L9A8wFM5Jyb+llC1w6pjv5yJP2rlFn8+qGEjliDU5rnwSbqDIT6PwYAnKzfcEgzoj9g2dT1pS27wTnPMlGPWCHGmBfUyZwv1IPQQznn1f0coWuHVEe/563q+DeLXz+U0BFrkKh57sIY622kTpTmWQVgc/2HRJqJfQDyNF9HiVXQLOY5SvMyUawOsV6MMWcAW6Be46scQBTnfK8Zp9K1Q6oTqPd11XVULX79UEJHrMFaAEWar9+vWsgY8wPwvOZlbDV7MxKiwzkvB/Cd5mU4Y+xhkWpLAcghvhYZsWKa1fwTod5dRgXgKc65WX8Q0rXTsjHGTC5gzxhzA/Cx5mUBgB365fVx/bAq+5wT0iQxxhbj72Tud83Xd6Hujl4O9TiDQgADOeeXRRshzRZjrAcAZ71DzwJ4BupV2kdVqX5Ks7OI9lxPAGcB+EA9UPlVACma9v6peQDA/zjnz4M0C4wxKYBY/L0902sA/mfiFBXnvEj/AF07LRdjrALqPwbioL59eg/qPwraQD0B4g0A7TXVX+acrxBpw6LXDyV0xCowxhjUf808baRKIYBHzf3rmjQvjLHdECZuxvhzztOqnB8AIAlAKyPnbAEQrp8IEuum6dlPra6enuuccz+RdujaaYEYY+YkTyUAFnPOvzDRjsWuH7rlSqwCV3sGQASArVAvKVAK9Q/kVQD6UTJHaotzfhxAHwCfQT37rBjAfajHuTwNYBL9QiZi6NppsQKhviW6E8BVqDsVyqD+3bQXwDIAXU0lc4Blrx/qoSOEEEIIsXLUQ0cIIYQQYuUooSOEEEIIsXKU0BFCCCGEWDlK6AghhBBCrBwldIQQQgghVo4SOkIIIYQQK0cJHSGEEEKIlaOEjhBCCCHEylFCRwghhBBi5WwaOwBCCLEWZu7faBTnnFkqFkII0Uc9dIQQQgAAjLHZjDFe18S1uWCM+Wk/D8bY6MaOhxBTKKEjhBDzORl5TNKr84KJeoQQUi/olishhJiJc14odpwxVqz3stRYPUIIqS/UQ0cIIYQQ8v/t3XvsX/Mdx/HnC0XNpS6lFuQnLt1G01aXylzaalWMdCYkNhupiFuCiJBYGSUxIUFSy4hrGZvLtoi5ZHOrdQsqy+ouiFUtWu2qVvy2+am3Pz6fb3oc53t+l7bf3/ckr0fyybl8LufzPWnadz/nfD6n4RzQmZltZJLm5vewluTj8ZLukrRU0meSFlfUOU7Sg5Lel/R/SaskPZXfc2v7d7ekcZIukfQXSSsl9UlaLen5fH5URZ2e/N7cHYVzUUoLCnnTCud7JG0v6WpJb0rqlfSepNsl7V6os6mk03M/PpK0RtKTkqYO4P6NkvRzSYskfZjvx7v5Hk6oqbcg93F+Pp4p6bF8X/4n6XVJl0oaWVF3CfDPwqmny/ekv36bdZIfuZqZdZCk44B7gC3a5G8HPADMLGXtAByW00mSfhgRH5fqjgcWVzQ7Cpic06mSjoiIt9bjZxTtAdwJ9BTO7QacAsyUdDCwAvgdcHSp7nRgiqRjIuLRqsYlHQr8Adip4ronAT+RdF5E3FDXSUkXAb8AijONvwVcDsyQNCMiPq9rw6ybeYTOzKxztgfmA68DPwB2IQUmPwPII28PkoK5XuAyYBwpmNsbuDCfnw7cVtF+AM8BFwCHAHuRAqFxwLnAUlLgda+kYmDzLmnSxpmFc+UJHd9v85vuBEaQgqtv5nQ20EcK7K4GrgJmAHOAfXKfZgHvkwYWbpY0otywpP2AP+XyLwM/zvdrR+B7wO9J/47Nk1QOFoumkoK535KC2h2B/YDf5PwpwBmlOt/JZVqOqrgnZt0jIpycnJyc1iMB00jBVACzK/LnFvJfBbZu087pucznwNQ2ZQ4DvsjlDhxkP8cAH+a6h1fkz271cxC/dzXQU1Hmipy/NqdZFWUOL7RzZEX+33Lei8DINn2Zn8u8BqiUt6DQ/i8r6gp4IecvqsjvKdSfNtx/zpyc6pJH6MzMOuvSaD8L9py8vTUinqkqEBFPA0/lwxMHc+GIWA48kQ9nDKZujXkRsaTi/H15uwmwMCL+WFHmSeDfeX9yMUPSJOCgfHhGRBRnEhddkrffBia0KfMpaXTwKyIigLvz4YSqUUKzpvA7dGZmnROkR4hfI2lnYP98uFDS1jXtvEwKyCZVtLMJcEJOBwCjgS0r2th34N2u9ec2598p7D9eVSAiQtI7pEeqY0rZ0/N2DfBazf34CFhJ+p2TgH9UlHkuIta0qd96l3AE6ZH4ijblzLqaAzozs85ZWTM6Vwyw7m5Tpmx08UDSNsDDpHfC+rPdAK/Rn+VVJyPiv4XX9CrLZK2Rt/JM07F5uy3wnwH2ZXSb88tq6vQW9r8229WsKfzI1cysc9o9NoShBVjlmbLXk4K5AG4FjgT2JE2qaL3I35oIsKH+Q792A5Upf+d2Q9yPwVy/qg9mjeEROjOz7vBpYX9iRCweTGVJ3wB+mg+vioiLa8o1Qet+vBgRE4azI2ZN4BE6M7PuUHznbOIQ6o9l3QjV/TXl9q/J6yat+zG2auFfM/sqB3RmZl0gIpYCb+bD2UNoovi4cdOqApImk9ama6evULayjQ5qTaTYEvjRMPWhr7A/3PfDrJYDOjOz7nF93k6RdH5dQUnbSNq1cGpJYX9WRfmtgF/1c/1Vhf1d25bqgIh4Fng+H14jqXZWrqSxdflDtJr0PiIM8/0w648DOjOz7nEL69aJu1bSA5KOkDQmfy91b0nHSroZeA84uFUxIpYBf82HcyTNyeVHSzoq501k3ShglcWkRYsB5kraXdIISZsN04jdqcAnpGVNFuXvuY6XtIOknSUdIOlMSU+QFgjeoCKiF3gjH56dv5M7Mt8Pv4NuXcUBnZlZl4iItcCxpE9aARxPWrduGekLD2+Rvmt6GmkW6GelJs4ircu2OXBlLr8CeIS06O6FwLM1119O+o4spGBqab5GH2kR4I6KiFdJ6+39i/R7ryAFnauAD4C/AzfmMn3Vray3eXl7IPASaZmTvo14PbMhcUBnZtZFIuKTiDietLDur0mTA1pBxAfAM6Rvv+4bEQ+V6r4CfDfXW57rLCN9H3Z6RFw3gC7MJn2qrBW8DKuIWERao+8c0ujlCtLv6gXeJk0AOZH0ma6Ncf2bgJOBhaRg+YvaCmbDROnLJ2ZmZmbWVB6hMzMzM2s4B3RmZmZmDeeAzszMzKzhHNCZmZmZNZwDOjMzM7OGc0BnZmZm1nAO6MzMzMwazgGdmZmZWcM5oDMzMzNrOAd0ZmZmZg3ngM7MzMys4RzQmZmZmTXcl9tWzQsbC3/rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mys_estimate = mys_model.estimate_effect(mys_identified_estimand,\n",
    "        method_name=\"backdoor.linear_regression\")\n",
    "print(mys_estimate)\n",
    "print(\"Causal Estimate is \" + str(mys_estimate.value))\n",
    "\n",
    "# Plot Slope of line between action and outcome = causal effect\n",
    "dowhy.plotter.plot_causal_effect(mys_estimate, df[data_dict[\"treatment_name\"]], df[data_dict[\"outcome_name\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "     d                               \n",
      "────────────(Expectation(Outcome|w0))\n",
      "d[Treatment]                         \n",
      "Estimand assumption 1, Unconfoundedness: If U→{Treatment} and U→Outcome then P(Outcome|Treatment,w0,U) = P(Outcome|Treatment,w0)\n",
      "\n",
      "## Realized estimand\n",
      "b: Outcome~Treatment+w0 | \n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 0.9924092476458988\n",
      "Effect estimates: [0.99240925 0.99240925 0.99240925 ... 0.99240925 0.99240925 0.99240925]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mys_dml_estimate = mys_model.estimate_effect(mys_identified_estimand, \n",
    "                                             method_name=\"backdoor.econml.dml.DML\",\n",
    "                                     control_value = 0,\n",
    "                                     treatment_value = 1,\n",
    "                                 confidence_intervals=False,\n",
    "                                method_params={\"init_params\":{'model_y':GradientBoostingRegressor(),\n",
    "                                                              'model_t': GradientBoostingRegressor(),\n",
    "                                                              \"model_final\":LassoCV(fit_intercept=False),\n",
    "                                                              'featurizer':PolynomialFeatures(degree=2, include_bias=True)},\n",
    "                                               \"fit_params\":{}})\n",
    "print(mys_dml_estimate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: for non-linear relationships, causal inference with DoubleML obtains a better estimate, which is closer to the causal effect of 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A high-level Pandas API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='v0'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import dowhy.api\n",
    "import dowhy.datasets\n",
    "\n",
    "data = dowhy.datasets.linear_dataset(beta=5,\n",
    "    num_common_causes=1,\n",
    "    num_instruments = 0,\n",
    "    num_samples=1000,\n",
    "    treatment_is_binary=True)\n",
    "\n",
    "# data['df'] is just a regular pandas.DataFrame\n",
    "data['df'].causal.do(x='v0', # name of treatment variable\n",
    "                     variable_types={'v0': 'b', 'y': 'c', 'W0': 'c'},\n",
    "                     outcome='y',\n",
    "                     common_causes=['W0']).groupby('v0').mean().plot(y='y', kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "* Four steps of causal inference\n",
    "    * Modelling\n",
    "    * Identification\n",
    "    * Estimation\n",
    "    * Refutation\n",
    "* DoWhy and EconML integrates causal infernece with machine learning\n",
    "* DoWhy comes with a Panda API to allow easy-to-access causal inference with data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "causal",
   "language": "python",
   "name": "causal"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}