diff --git a/.readthedocs.yaml b/.readthedocs.yaml deleted file mode 100644 index a3f3c3f..0000000 --- a/.readthedocs.yaml +++ /dev/null @@ -1,11 +0,0 @@ -version: 2 - -build: - os: ubuntu-20.04 - tools: - python: "3.9" - -python: - install: - - requirements: doc/requirements.txt - - requirements: requirements.txt \ No newline at end of file diff --git a/README.md b/README.md index 3a6b08a..d7dda63 100644 --- a/README.md +++ b/README.md @@ -45,19 +45,6 @@ att_gt.fit(formula='y') att_gt.aggregate('event') ``` -*differences* ATTgt benefitted substantially from the original authors' R packages: Callaway & Sant'Anna's [did](https://github.com/bcallaway11/did) and Sant'Anna and -Zhao's [DRDID](https://github.com/pedrohcgs/DRDID) - -> **_NOTE:_** Important note on performance ! Currently, the ATTgt class allows users to pass string entity identifiers, as in the example with `df = simulate_data()` above, where the first index containing the entity identifiers is a string datatype. Note that the performance of the ATT computation (when calling `.fit()`) would improve greatly if you cast the entities to integers before initializing ATTgt. You can easily do that just by using pandas category codes. - -### TWFE - -```python -from differences import TWFE, simulate_data - -df = simulate_data() - -twfe = TWFE(data=df, cohort_name='cohort') - -twfe.fit(formula='y') -``` +*differences* ATTgt benefited from + - Callaway & Sant'Anna's [did](https://github.com/bcallaway11/did) + - Sant'Anna and Zhao's [DRDID](https://github.com/pedrohcgs/DRDID) diff --git a/differences/tests/_attgt.py b/differences/tests/_attgt.py deleted file mode 100644 index 6bb27bb..0000000 --- a/differences/tests/_attgt.py +++ /dev/null @@ -1,45 +0,0 @@ -from __future__ import annotations - -import pandas as pd - -pd.set_option("display.max_rows", 6) - -from differences import ATTgt, load_data -from differences.tests._utility import get_r_did_result - -data_name = "rc_multi_period" - -params_tuple = (0, "varying", "nevertreated", "dr-mle", "random_weights") -anticipation, base_period, control_group, est_method, weights_name = params_tuple - -R_result = get_r_did_result( - data_name=data_name, - params_tuple=params_tuple, - result_type="att_gt", -) - -data_dict = getattr(load_data, data_name)() - -att_gt = ATTgt( - data=data_dict["data"], - cohort_name=data_dict["cohort_name"], - anticipation=anticipation, - base_period=base_period, -) - -att_gt.fit( - formula=f"{data_dict['y_name']}", - control_group=control_group, - weights_name=weights_name, - est_method=est_method, -) - -agg_type = "time" -att_gt.aggregate(agg_type, overall=True) - -get_r_did_result( - data_name=data_name, - params_tuple=params_tuple, - result_type="overall", - agg_type=agg_type, -) diff --git a/differences/tests/_twfe.py b/differences/tests/_twfe.py deleted file mode 100644 index 25b6560..0000000 --- a/differences/tests/_twfe.py +++ /dev/null @@ -1,23 +0,0 @@ -from __future__ import annotations - -from altair import HConcatChart -from pandas import DataFrame - -from differences import TWFE, simulate_data - -panel_data = simulate_data(nentity=20) # generate data - -twfe = TWFE( - data=panel_data, - cohort_name="cohort", -) - -res = twfe.fit( - formula="y", - cluster_names="entity", -) - -assert isinstance(res, DataFrame) -assert len(res) - -assert isinstance(twfe.plot(), HConcatChart) diff --git a/differences/tests/_utility.py b/differences/tests/_utility.py deleted file mode 100644 index b607f5b..0000000 --- a/differences/tests/_utility.py +++ /dev/null @@ -1,74 +0,0 @@ -from __future__ import annotations - -import pickle -from itertools import product -from os.path import join -from typing import NamedTuple - -from pandas import DataFrame - -from differences.datasets import load_data - -py_test_data = { - "mpdta": load_data.mpdta(), - "rc_multi_period": load_data.rc_multi_period(), -} - - -def flatten_res(df): - df.columns = [c[2] for c in list(df)] - return df.reset_index() - - -class RATTgtArgs(NamedTuple): - aggregation_type = ["simple", "dynamic", "group", "calendar"] - - python_agg_types = { - "group": "cohort", - "dynamic": "event", - "calendar": "time", - "simple": "simple", - } - r_est_method = {"dr-mle": "dr", "reg": "reg", "std_ipw-mle": "ipw"} - - dataset_names = ["mpdta", "rc_multi_period"] - - varying_args = list( - product( - ["mpdta", "rc_multi_period"], - [0, 1, 2], # anticipation - ["varying", "universal"], # base_period - ["nevertreated", "notyettreated"], # control_group - ["dr-mle", "std_ipw-mle", "reg"], # est method - [None, "random_weights"], # weights - ) - ) - - -def read_r_did_results(): - with open( - f"{join(load_data._get_path(__file__), 'r_did_results')}", "rb" - ) as handle: - res = pickle.load(handle) - return res - - -r_did_results = read_r_did_results() - - -def get_r_did_result( - data_name: str, # 'mpdta', 'rc_multi_period' - params_tuple: tuple, # anticipation, base_period, control_group, est_method, weights - result_type: str, # 'att_gt', 'aggregate', 'overall' - agg_type: str = None, # 'simple', 'dynamic', 'group', 'calendar' -): - res = r_did_results[data_name][params_tuple][result_type] - - if result_type in ["aggregate", "overall"]: - if agg_type is None: - raise ValueError( - "need to provide an agg_type: 'simple', 'dynamic', 'group', 'calendar'" - ) - res = res[agg_type] - - return DataFrame.from_dict(res) diff --git a/differences/tests/r_did_results b/differences/tests/r_did_results deleted file mode 100644 index 7d83eb7..0000000 Binary files a/differences/tests/r_did_results and /dev/null differ diff --git a/differences/tests/test_attgt.py b/differences/tests/test_attgt.py deleted file mode 100644 index 8fac452..0000000 --- a/differences/tests/test_attgt.py +++ /dev/null @@ -1,119 +0,0 @@ -from __future__ import annotations - -import numpy as np -import pytest - -from differences import ATTgt -from differences.tests._utility import (RATTgtArgs, flatten_res, - get_r_did_result, py_test_data) - - -@pytest.mark.parametrize( - "data_name,anticipation,base_period,control_group,est_method,weights_name", - RATTgtArgs.varying_args, -) -def test_att_gt_agains_r_did( - data_name, anticipation, base_period, control_group, est_method, weights_name -) -> None: - # ------------------------- R -------------------------------------- - - R_result = get_r_did_result( - data_name=data_name, - params_tuple=( - anticipation, - base_period, - control_group, - est_method, - weights_name, - ), - result_type="att_gt", - ) - # ------------------------- python --------------------------------- - - att_gt = ATTgt( - data=py_test_data[data_name]["data"], - cohort_name=py_test_data[data_name]["cohort_name"], - anticipation=anticipation, - base_period=base_period, - ) - - py_result = att_gt.fit( - formula=f"{py_test_data[data_name]['y_name']}", - control_group=control_group, - weights_name=weights_name, - est_method=est_method, - ) - - py_result = flatten_res(py_result) - - # ----------------------- compare att gt --------------------------- - - assert np.allclose( - R_result["att"].dropna().to_numpy(), py_result["ATT"].dropna().to_numpy() - ) - - assert np.allclose( - R_result["se"].dropna().to_numpy(), py_result["std_error"].dropna().to_numpy() - ) - - # ----------------------- aggregate -------------------------------- - - for agg_type in ["event", "simple", "time", "cohort"]: - - # load pre calculated R results - - R_agg_res = get_r_did_result( - data_name=data_name, - params_tuple=( - anticipation, - base_period, - control_group, - est_method, - weights_name, - ), - result_type="aggregate", - agg_type=agg_type, - ) - - R_agg_overall_result = get_r_did_result( - data_name=data_name, - params_tuple=( - anticipation, - base_period, - control_group, - est_method, - weights_name, - ), - result_type="overall", - agg_type=agg_type, - ) - - # ------------------------ overall ----------------------------- - - py_agg_overall_result = flatten_res(att_gt.aggregate(agg_type, overall=True)) - - assert np.allclose( - R_agg_overall_result["att"].dropna().to_numpy(), - py_agg_overall_result["ATT"].dropna().to_numpy(), - ) - - if agg_type != "cohort": # group_overall likely a bug in R - assert np.allclose( - R_agg_overall_result["se"].dropna().to_numpy(), - py_agg_overall_result["std_error"].dropna().to_numpy(), - ) - - if agg_type != "simple": - # ------------------------ not overall --------------------- - - py_agg_result = flatten_res(att_gt.aggregate(agg_type)) # py - - assert np.allclose( - R_agg_res["att"].dropna().to_numpy(), - py_agg_result["ATT"].dropna().to_numpy(), - ) - - assert np.allclose( - R_agg_res["se"].dropna().to_numpy(), - py_agg_result["std_error"].dropna().to_numpy(), - ) diff --git a/differences/tests/test_twfe.py b/differences/tests/test_twfe.py deleted file mode 100644 index 0dee2cc..0000000 --- a/differences/tests/test_twfe.py +++ /dev/null @@ -1,26 +0,0 @@ -from __future__ import annotations - -from altair import HConcatChart -from pandas import DataFrame - -from differences import TWFE, simulate_data - -panel_data = simulate_data(nentity=20) # generate data - - -def test_basic_twfe(): - twfe = TWFE( - data=panel_data, - cohort_name="cohort", - ) - - res = twfe.fit( - formula="y", - cluster_names="entity", - ) - - assert isinstance(res, DataFrame) - - assert len(res) - - assert isinstance(twfe.plot(), HConcatChart) diff --git a/differences/tools/feols/__init__.py b/differences/tools/feols/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/differences/tools/r_install.py b/differences/tools/r_install.py deleted file mode 100644 index ee6c6a8..0000000 --- a/differences/tools/r_install.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Module to install r-packages -""" -from __future__ import annotations - -import argparse - -import rpy2.robjects.packages as rpackages -from rpy2.robjects.vectors import StrVector # R vector of strings - -required = [ - "did", -] -# ---------------------------------------------------------------------- - -# import R's utility package -r_utils = rpackages.importr("utils") - -# select a mirror for R packages -r_utils.chooseCRANmirror(ind=1) # select the first mirror in the list - - -def install_rpackages(r_pkgs: list = None): - # Selectively install what needs to be installed. - - if r_pkgs is None: - r_pkgs = required - else: - r_pkgs += required - - if not r_pkgs: - print("no library specified") - - else: - names_to_install = [x for x in set(r_pkgs) if not rpackages.isinstalled(x)] - - if names_to_install: - names = ", ".join(names_to_install) - print(f"the following R libraries will be installed: {names}") - r_utils.install_packages(StrVector(names_to_install)) - - else: - print("all libraries already installed") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "-l", - "--list", - nargs="+", - required=False, - help="A list or R packages to install from CRAN", - ) - args = parser.parse_args() - - install_rpackages(r_pkgs=args.list) diff --git a/doc/requirements.txt b/doc/requirements.txt deleted file mode 100644 index 29f55fb..0000000 --- a/doc/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -furo -sphinx_copybutton -jupyter -sphinx -nbsphinx diff --git a/doc/source/_static/css/custom.css b/doc/source/_static/css/custom.css deleted file mode 100644 index 4410ab8..0000000 --- a/doc/source/_static/css/custom.css +++ /dev/null @@ -1,3 +0,0 @@ -.underline { - text-decoration: underline; -} \ No newline at end of file diff --git a/doc/source/api_reference/attgt.rst b/doc/source/api_reference/attgt.rst deleted file mode 100644 index 49cff6c..0000000 --- a/doc/source/api_reference/attgt.rst +++ /dev/null @@ -1,15 +0,0 @@ -ATTgt -===== - -.. autoclass:: differences.attgt.attgt.ATTgt - :members: - - -**Citation** - -.. [CS2021] Callaway, Brantly, and Pedro HC Sant’Anna. "Difference-in-differences with multiple - time periods." *Journal of Econometrics* 225, no. 2 (2021): 200-230. -.. [CGS2022] Callaway, Brantly, Andrew Goodman-Bacon, and Pedro HC Sant'Anna. - "Difference-in-differences with a continuous treatment." *arXiv preprint* arXiv:2107.02637 (2021). -.. [SZ2020] Sant’Anna, Pedro HC, and Jun Zhao. "Doubly robust difference-in-differences - estimators." *Journal of Econometrics* 219, no. 1 (2020): 101-122. \ No newline at end of file diff --git a/doc/source/api_reference/index.rst b/doc/source/api_reference/index.rst deleted file mode 100644 index d2a3118..0000000 --- a/doc/source/api_reference/index.rst +++ /dev/null @@ -1,7 +0,0 @@ -API reference -============= - -.. toctree:: - - attgt - twfe \ No newline at end of file diff --git a/doc/source/api_reference/twfe.rst b/doc/source/api_reference/twfe.rst deleted file mode 100644 index 5a45291..0000000 --- a/doc/source/api_reference/twfe.rst +++ /dev/null @@ -1,6 +0,0 @@ -TWFE -==== - - -.. autoclass:: differences.twfe.twfe.TWFE - :members: diff --git a/doc/source/conf.py b/doc/source/conf.py deleted file mode 100644 index 168691f..0000000 --- a/doc/source/conf.py +++ /dev/null @@ -1,124 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -from __future__ import annotations - -import glob -import hashlib -import os -import sys - -sys.path.insert(0, os.path.abspath(".")) -sys.path.insert(0, os.path.abspath("../..")) - -# -- Project information ----------------------------------------------- - -project = "differences" -copyright = "2022, Bernardo Dionsi" -author = "Bernardo Dionsi" - -# The full version, including alpha/beta/rc tags -release = "0.1.0" - -# -- General configuration --------------------------------------------- - -autodoc_member_order = "bysource" - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - # 'sphinx.ext.viewcode', - "sphinx_copybutton", - "sphinx.ext.doctest", - "sphinx.ext.napoleon", - "sphinx.ext.intersphinx", # provides links for classes in type annotations - "nbsphinx", - "altair.sphinxext.altairplot", -] - -autosummary_generate = True - -# Add any paths that contain templates here, relative to this directory. -# templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] - -# ------------------------ Options for HTML output --------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = "furo" - -html_theme_options = { - "sidebar_hide_name": True, -} -html_title = "differences" - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] - -html_logo = "images/logo/bw/logo_name_bw.png" -# html_logo = "images/logo/bw/logo_bw.png" - -# These paths are either relative to html_static_path -# or fully qualified paths (eg. https://...) -html_css_files = [ - "css/custom.css", -] - -# todo: ADD rst_prolog to global -# https://stackoverflow.com/questions/9698702/how-do-i-create-a-global-role-roles-in-sphinx -# rst_prolog = """ -# .. role:: underline -# :class: underline -# """ - -rst_prolog = """ -.. role:: underline -""" - -# ---------------------------------------------------------------------- - -# from linearmodels: copy over notebooks from examples to docs for build -files = glob.glob("../../examples/*.ipynb") -print(files) -for file_to_copy in files: - file_name = os.path.split(file_to_copy)[-1] - - out_dir = "notebooks" - - if not os.path.exists(out_dir): - os.makedirs(out_dir, exist_ok=True) - - out_file = os.path.join(out_dir, file_name) - existing_hash = "" - with open(file_to_copy, "rb") as example: - example_file = example.read() - example_hash = hashlib.sha512(example_file).hexdigest() - - if os.path.exists(out_file): - with open(out_file, "rb") as existing: - existing_hash = hashlib.sha512(existing.read()).hexdigest() - - if existing_hash != example_hash: - print(f"Copying {file_to_copy} to {out_file}") - with open(out_file, "wb") as out: - out.write(example_file) diff --git a/doc/source/example_notebooks/index.rst b/doc/source/example_notebooks/index.rst deleted file mode 100644 index 1f57f81..0000000 --- a/doc/source/example_notebooks/index.rst +++ /dev/null @@ -1,6 +0,0 @@ -Examples -======== - -.. toctree:: - ../notebooks/attgt.ipynb - ../notebooks/twfe.ipynb \ No newline at end of file diff --git a/doc/source/getting_started/index.rst b/doc/source/getting_started/index.rst deleted file mode 100644 index 36cc31f..0000000 --- a/doc/source/getting_started/index.rst +++ /dev/null @@ -1,10 +0,0 @@ -Getting started -=============== - -**Installation** - -Install *differences* from PyPI with pip: - -.. code-block:: bash - - pip install differences diff --git a/doc/source/images/logo/bw/logo_bw.png b/doc/source/images/logo/bw/logo_bw.png deleted file mode 100644 index 373f8e9..0000000 Binary files a/doc/source/images/logo/bw/logo_bw.png and /dev/null differ diff --git a/doc/source/images/logo/bw/logo_name_bw.png b/doc/source/images/logo/bw/logo_name_bw.png deleted file mode 100644 index 7fbcc26..0000000 Binary files a/doc/source/images/logo/bw/logo_name_bw.png and /dev/null differ diff --git a/doc/source/index.rst b/doc/source/index.rst deleted file mode 100644 index 92ff63a..0000000 --- a/doc/source/index.rst +++ /dev/null @@ -1,20 +0,0 @@ -Difference in Differences -========================= - -.. toctree:: - :hidden: - :maxdepth: 3 - - getting_started/index - example_notebooks/index - api_reference/index - - -*differences* contains a set of classes and functions for **difference-in-differences** and **event study** estimation in Python. - -.. Indices and tables -.. ================== - -.. * :ref:`genindex` - * :ref:`modindex` - * :ref:`search` diff --git a/doc/source/notebooks/attgt.ipynb b/doc/source/notebooks/attgt.ipynb deleted file mode 100644 index a5b3fa1..0000000 --- a/doc/source/notebooks/attgt.ipynb +++ /dev/null @@ -1,3828 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3540d2e8", - "metadata": {}, - "source": [ - "# ATTgt" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8db6ab5a", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "pd.set_option(\"display.max_rows\", 6)\n", - "\n", - "from differences import simulate_data, ATTgt" - ] - }, - { - "cell_type": "markdown", - "id": "ea05e82e", - "metadata": {}, - "source": [ - "see [plotto](https://plotto.readthedocs.io/en/latest/notebooks/plotto.html#Plot-parameters) to configure the plots. the .plot() method takes the same arguments as the function [mark_plot() in plotto](https://plotto.readthedocs.io/en/latest/api_reference/plotto.html#plotto.plotto.mark_plot)" - ] - }, - { - "cell_type": "markdown", - "id": "ee745c62", - "metadata": {}, - "source": [ - "## Binary Treatment" - ] - }, - { - "cell_type": "markdown", - "id": "4a477966", - "metadata": {}, - "source": [ - "An entity can be either treated or not treated, and once treated it remains treated, the groups are identified by the date of the treatement (cohort)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "879da0fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " y x0 w cat.0 cat.1 effect cohort \\\n", - "entity time \n", - "e0 1900 4.295747 0.160245 3.729827 0 0 0.00 1904.0 \n", - " 1901 -4.799873 -1.933404 1.691201 0 0 0.00 1904.0 \n", - " 1902 11.082130 -0.056813 1.786326 0 0 0.00 1904.0 \n", - "... ... ... ... ... ... ... ... \n", - "e999 1905 -7.629120 -0.258604 1.801049 0 0 5.25 1904.0 \n", - " 1906 39.241097 1.504564 0.616657 0 0 10.50 1904.0 \n", - " 1907 23.619567 0.472740 0.674045 0 0 15.75 1904.0 \n", - "\n", - " intensity \n", - "entity time \n", - "e0 1900 5.25 \n", - " 1901 5.25 \n", - " 1902 5.25 \n", - "... ... \n", - "e999 1905 5.25 \n", - " 1906 5.25 \n", - " 1907 5.25 \n", - "\n", - "[8000 rows x 8 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "panel_data = simulate_data() # generate data\n", - "\n", - "panel_data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d6f7a10f", - "metadata": {}, - "outputs": [], - "source": [ - "att_gt = ATTgt(data=panel_data, cohort_name=\"cohort\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d3b72951", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Computing ATTgt [workers=1] 100%|████████████████████| 14/14 [00:00<00:00, 59.11it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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ATTgtResult
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
cohortbase_periodtime
1903190019010.4470311.407877-2.3123573.206418
190119020.1968061.293725-2.3388492.732461
190219032.6138231.2957130.0742725.153373*
........................
1904190319053.7019551.3524061.0512886.352621*
190610.6377811.3022438.08543113.190131*
190713.8565751.35372611.20332016.509830*
\n", - "

14 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " ATTgtResult \\\n", - " analytic pointwise conf. band \n", - " ATT std_error lower upper \n", - "cohort base_period time \n", - "1903 1900 1901 0.447031 1.407877 -2.312357 3.206418 \n", - " 1901 1902 0.196806 1.293725 -2.338849 2.732461 \n", - " 1902 1903 2.613823 1.295713 0.074272 5.153373 \n", - "... ... ... ... ... \n", - "1904 1903 1905 3.701955 1.352406 1.051288 6.352621 \n", - " 1906 10.637781 1.302243 8.085431 13.190131 \n", - " 1907 13.856575 1.353726 11.203320 16.509830 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "cohort base_period time \n", - "1903 1900 1901 \n", - " 1901 1902 \n", - " 1902 1903 * \n", - "... ... \n", - "1904 1903 1905 * \n", - " 1906 * \n", - " 1907 * \n", - "\n", - "[14 rows x 5 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.fit(\n", - " formula=\"y ~ x0\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d19e9afc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.plot(configure_axisX={\"format\": \"c\"})" - ] - }, - { - "cell_type": "markdown", - "id": "ab0ec44b", - "metadata": {}, - "source": [ - "### Aggregate" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a977132e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"time\")\n", - "att_gt.plot(\"time\", configure_axisX={\"format\": \"c\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f0321319", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"event\")\n", - "att_gt.plot(\"event\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cd3edc08", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"cohort\")\n", - "att_gt.plot(\"cohort\", lines=False, configure_axisX={\"format\": \"c\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9a0cf76b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SimpleAggregation
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
04.0089940.7278172.5824985.43549*
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" - ], - "text/plain": [ - " SimpleAggregation \n", - " analytic pointwise conf. band \n", - " ATT std_error lower upper zero_not_in_cband\n", - "0 4.008994 0.727817 2.582498 5.43549 *" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"simple\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "09c79b30", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SimpleAggregation
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
04.0089940.7278172.5824985.43549*
\n", - "
" - ], - "text/plain": [ - " SimpleAggregation \n", - " analytic pointwise conf. band \n", - " ATT std_error lower upper zero_not_in_cband\n", - "0 4.008994 0.727817 2.582498 5.43549 *" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"simple\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "52a93a69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EventAggregationOverall
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
03.7756620.7421542.3210685.230257*
\n", - "
" - ], - "text/plain": [ - " EventAggregationOverall \\\n", - " analytic pointwise conf. band \n", - " ATT std_error lower upper \n", - "0 3.775662 0.742154 2.321068 5.230257 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "0 * " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"event\", overall=True)" - ] - }, - { - "cell_type": "markdown", - "id": "02a75d4c", - "metadata": {}, - "source": [ - "### Heterogeneity & Triple Difference" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "61b59315", - "metadata": {}, - "outputs": [], - "source": [ - "panel_data = simulate_data(samples=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "65a8eb25", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Computing ATTgt for samples = 0 [workers=1]100%|████████████████████| 21/21 [00:00<00:00, 119.56it/s]\n", - "Computing ATTgt for samples = 1 [workers=1]100%|████████████████████| 21/21 [00:00<00:00, 116.07it/s]\n", - "Computing ATTgt for samples = 2 [workers=1]100%|████████████████████| 21/21 [00:00<00:00, 115.64it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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ATTgtResult
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
sample_namecohortbase_periodtime
samples = 0190219001901-0.4131632.285396-4.8924584.066131
190119020.0169272.219508-4.3332284.367082
19038.4106512.1741434.14940812.671893*
...........................
samples = 2190519041905-2.2660162.272358-6.7197562.187723
19064.8533772.2913640.3623859.344369*
190712.1468942.2034727.82816816.465620*
\n", - "

63 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " ATTgtResult \\\n", - " analytic \n", - " ATT std_error \n", - "sample_name cohort base_period time \n", - "samples = 0 1902 1900 1901 -0.413163 2.285396 \n", - " 1901 1902 0.016927 2.219508 \n", - " 1903 8.410651 2.174143 \n", - "... ... ... \n", - "samples = 2 1905 1904 1905 -2.266016 2.272358 \n", - " 1906 4.853377 2.291364 \n", - " 1907 12.146894 2.203472 \n", - "\n", - " \\\n", - " pointwise conf. band \n", - " lower upper \n", - "sample_name cohort base_period time \n", - "samples = 0 1902 1900 1901 -4.892458 4.066131 \n", - " 1901 1902 -4.333228 4.367082 \n", - " 1903 4.149408 12.671893 \n", - "... ... ... \n", - "samples = 2 1905 1904 1905 -6.719756 2.187723 \n", - " 1906 0.362385 9.344369 \n", - " 1907 7.828168 16.465620 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "sample_name cohort base_period time \n", - "samples = 0 1902 1900 1901 \n", - " 1901 1902 \n", - " 1903 * \n", - "... ... \n", - "samples = 2 1905 1904 1905 \n", - " 1906 * \n", - " 1907 * \n", - "\n", - "[63 rows x 5 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt = ATTgt(data=panel_data, cohort_name=\"cohort\")\n", - "\n", - "# heterogeneity\n", - "\n", - "att_gt.fit(formula=\"y\", split_sample_by=\"samples\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6bca34b2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"event\")\n", - "\n", - "att_gt.plot(\"event\", vertical_line=0, horizontal_line=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "92e11306", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SimpleAggregation
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
sample_name
samples = 011.6595251.1457029.41399013.905061*
samples = 113.0360151.23169610.62193515.450095*
samples = 214.4912061.28452911.97357617.008836*
\n", - "
" - ], - "text/plain": [ - " SimpleAggregation \\\n", - " analytic pointwise conf. band \n", - " ATT std_error lower upper \n", - "sample_name \n", - "samples = 0 11.659525 1.145702 9.413990 13.905061 \n", - "samples = 1 13.036015 1.231696 10.621935 15.450095 \n", - "samples = 2 14.491206 1.284529 11.973576 17.008836 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "sample_name \n", - "samples = 0 * \n", - "samples = 1 * \n", - "samples = 2 * " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"simple\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "4051c33c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.plot(\n", - " \"simple\",\n", - " y_title=[\"Sample\", \"Names\"],\n", - " vertical_line=0,\n", - " configure_axisY={\"domain\": True, \"ticks\": True},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "cfe8c034", - "metadata": {}, - "source": [ - "## Triple Difference" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7c407f69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['samples = 0', 'samples = 1', 'samples = 2']" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.sample_names" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1b6f8be6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DifferenceTimeAggregation
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
difference_betweentime
samples = 1 - samples = 219020.4781231.541316-2.5428013.499047
19035.5499081.4740242.6608748.438943*
1904-5.8693591.375390-8.565075-3.173643*
19051.9401281.286668-0.5816954.461951
1906-1.8885931.396793-4.6262580.849071
1907-5.3488801.394556-8.082160-2.615600*
\n", - "
" - ], - "text/plain": [ - " DifferenceTimeAggregation \\\n", - " analytic \n", - " ATT std_error \n", - "difference_between time \n", - "samples = 1 - samples = 2 1902 0.478123 1.541316 \n", - " 1903 5.549908 1.474024 \n", - " 1904 -5.869359 1.375390 \n", - " 1905 1.940128 1.286668 \n", - " 1906 -1.888593 1.396793 \n", - " 1907 -5.348880 1.394556 \n", - "\n", - " \\\n", - " pointwise conf. band \n", - " lower upper \n", - "difference_between time \n", - "samples = 1 - samples = 2 1902 -2.542801 3.499047 \n", - " 1903 2.660874 8.438943 \n", - " 1904 -8.565075 -3.173643 \n", - " 1905 -0.581695 4.461951 \n", - " 1906 -4.626258 0.849071 \n", - " 1907 -8.082160 -2.615600 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "difference_between time \n", - "samples = 1 - samples = 2 1902 \n", - " 1903 * \n", - " 1904 * \n", - " 1905 \n", - " 1906 \n", - " 1907 * " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"time\", difference=[\"samples = 1\", \"samples = 2\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9951ecb1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.plot(\"time\", difference=True)" - ] - }, - { - "cell_type": "markdown", - "id": "446d7705", - "metadata": {}, - "source": [ - "## Multi-valued Treatment" - ] - }, - { - "cell_type": "markdown", - "id": "aecee69d", - "metadata": {}, - "source": [ - "An entity can be treated with different intensities or not treated, the groups are identified by the date of the treatement (cohort) & the intensity which represents a second dimension." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "8832441a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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yx0wcat.0cat.1effectcohortstrataintensity
entitytime
e0190014.3201810.1602453.729827000.01904.00.010.0
19011.118288-1.9334041.691201000.01904.00.010.0
1902-0.252880-0.0568131.786326000.01904.00.010.0
.................................
e999190516.700577-0.2586041.801049000.51904.01.00.5
190615.1516841.5045640.616657001.01904.01.00.5
19070.1631860.4727400.674045001.51904.01.00.5
\n", - "

8000 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " y x0 w cat.0 cat.1 effect cohort \\\n", - "entity time \n", - "e0 1900 14.320181 0.160245 3.729827 0 0 0.0 1904.0 \n", - " 1901 1.118288 -1.933404 1.691201 0 0 0.0 1904.0 \n", - " 1902 -0.252880 -0.056813 1.786326 0 0 0.0 1904.0 \n", - "... ... ... ... ... ... ... ... \n", - "e999 1905 16.700577 -0.258604 1.801049 0 0 0.5 1904.0 \n", - " 1906 15.151684 1.504564 0.616657 0 0 1.0 1904.0 \n", - " 1907 0.163186 0.472740 0.674045 0 0 1.5 1904.0 \n", - "\n", - " strata intensity \n", - "entity time \n", - "e0 1900 0.0 10.0 \n", - " 1901 0.0 10.0 \n", - " 1902 0.0 10.0 \n", - "... ... ... \n", - "e999 1905 1.0 0.5 \n", - " 1906 1.0 0.5 \n", - " 1907 1.0 0.5 \n", - "\n", - "[8000 rows x 9 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "panel_data = simulate_data(intensity_by=2) # generate data\n", - "\n", - "panel_data" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "3734b6f5", - "metadata": {}, - "outputs": [], - "source": [ - "att_gt = ATTgt(data=panel_data, cohort_name=\"cohort\", strata_name=\"strata\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "41971182", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Computing ATTgt [workers=1] 100%|████████████████████| 42/42 [00:00<00:00, 44.39it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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ATTgtResult
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
stratumcohortbase_periodtime
0190119001901-1.1665681.488863-4.0846861.751550
19029.7431551.6281446.55205212.934258*
190319.4732301.62615716.28602022.660440*
...........................
11905190419051.4543681.619994-1.7207624.629498
19060.0307971.537388-2.9824283.044021
19071.2312281.572191-1.8502104.312665
\n", - "

42 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " ATTgtResult \\\n", - " analytic pointwise conf. band \n", - " ATT std_error lower \n", - "stratum cohort base_period time \n", - "0 1901 1900 1901 -1.166568 1.488863 -4.084686 \n", - " 1902 9.743155 1.628144 6.552052 \n", - " 1903 19.473230 1.626157 16.286020 \n", - "... ... ... ... \n", - "1 1905 1904 1905 1.454368 1.619994 -1.720762 \n", - " 1906 0.030797 1.537388 -2.982428 \n", - " 1907 1.231228 1.572191 -1.850210 \n", - "\n", - " \n", - " \n", - " upper zero_not_in_cband \n", - "stratum cohort base_period time \n", - "0 1901 1900 1901 1.751550 \n", - " 1902 12.934258 * \n", - " 1903 22.660440 * \n", - "... ... ... \n", - "1 1905 1904 1905 4.629498 \n", - " 1906 3.044021 \n", - " 1907 4.312665 \n", - "\n", - "[42 rows x 5 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.fit(formula=\"y\", n_jobs=1)" - ] - }, - { - "cell_type": "markdown", - "id": "920f63a2", - "metadata": {}, - "source": [ - "### Aggregate" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "789bcae6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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EventAggregation
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
stratumrelative_period
0-4-0.5282181.521623-3.5105452.454109
-31.5399601.009962-0.4395293.519448
-2-0.1385310.989853-2.0786071.801545
.....................
143.1889151.5767820.0984786.279352*
52.2720941.596461-0.8569135.401100
63.8960551.6268780.7074327.084679*
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22 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " EventAggregation \\\n", - " analytic pointwise conf. band \n", - " ATT std_error lower \n", - "stratum relative_period \n", - "0 -4 -0.528218 1.521623 -3.510545 \n", - " -3 1.539960 1.009962 -0.439529 \n", - " -2 -0.138531 0.989853 -2.078607 \n", - "... ... ... ... \n", - "1 4 3.188915 1.576782 0.098478 \n", - " 5 2.272094 1.596461 -0.856913 \n", - " 6 3.896055 1.626878 0.707432 \n", - "\n", - " \n", - " \n", - " upper zero_not_in_cband \n", - "stratum relative_period \n", - "0 -4 2.454109 \n", - " -3 3.519448 \n", - " -2 1.801545 \n", - "... ... ... \n", - "1 4 6.279352 * \n", - " 5 5.401100 \n", - " 6 7.084679 * \n", - "\n", - "[22 rows x 5 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"event\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "99bde70f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.plot(\"event\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5e7946e2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Bootstrap 100%|████████████████████| [00:00<00:00, 10926.23it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
DifferenceEventAggregation
bootstrapsimult. conf. band
ATTstd_errorlowerupperzero_not_in_cband
difference_betweenrelative_period
0 - 1-4-1.1214241.702570-5.9024283.659580
-31.8605881.272509-1.7127575.433934
-2-0.5970331.240433-4.0803062.886239
..................
437.0840821.71625332.26465541.903510*
547.3731461.62086342.82158451.924708*
655.7689561.69123751.01977760.518135*
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11 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " DifferenceEventAggregation \\\n", - " bootstrap \n", - " ATT std_error \n", - "difference_between relative_period \n", - "0 - 1 -4 -1.121424 1.702570 \n", - " -3 1.860588 1.272509 \n", - " -2 -0.597033 1.240433 \n", - "... ... ... \n", - " 4 37.084082 1.716253 \n", - " 5 47.373146 1.620863 \n", - " 6 55.768956 1.691237 \n", - "\n", - " \\\n", - " simult. conf. band \n", - " lower upper \n", - "difference_between relative_period \n", - "0 - 1 -4 -5.902428 3.659580 \n", - " -3 -1.712757 5.433934 \n", - " -2 -4.080306 2.886239 \n", - "... ... ... \n", - " 4 32.264655 41.903510 \n", - " 5 42.821584 51.924708 \n", - " 6 51.019777 60.518135 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "difference_between relative_period \n", - "0 - 1 -4 \n", - " -3 \n", - " -2 \n", - "... ... \n", - " 4 * \n", - " 5 * \n", - " 6 * \n", - "\n", - "[11 rows x 5 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"event\", difference=[0, 1], boot_iterations=5000)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "7fff0dfd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.plot(\"event\", difference=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "61047119", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"simple\")\n", - "att_gt.plot(\"simple\")" - ] - }, - { - "cell_type": "markdown", - "id": "b68b21ab", - "metadata": {}, - "source": [ - "### Heterogeneity & Triple Difference" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "17960802", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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yx0wcat.0cat.1effectcohortsamplesstrataintensity
entitytime
e019002.3474520.1602453.729827000.01906.021.04.3
1901-2.880331-1.9334041.691201000.01906.021.04.3
1902-6.399243-0.0568131.786326000.01906.021.04.3
....................................
e9991905-8.514995-0.2586041.801049000.01906.021.04.3
19063.7130391.5045640.616657000.01906.021.04.3
190726.9428320.4727400.674045004.31906.021.04.3
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8000 rows × 10 columns

\n", - "
" - ], - "text/plain": [ - " y x0 w cat.0 cat.1 effect cohort \\\n", - "entity time \n", - "e0 1900 2.347452 0.160245 3.729827 0 0 0.0 1906.0 \n", - " 1901 -2.880331 -1.933404 1.691201 0 0 0.0 1906.0 \n", - " 1902 -6.399243 -0.056813 1.786326 0 0 0.0 1906.0 \n", - "... ... ... ... ... ... ... ... \n", - "e999 1905 -8.514995 -0.258604 1.801049 0 0 0.0 1906.0 \n", - " 1906 3.713039 1.504564 0.616657 0 0 0.0 1906.0 \n", - " 1907 26.942832 0.472740 0.674045 0 0 4.3 1906.0 \n", - "\n", - " samples strata intensity \n", - "entity time \n", - "e0 1900 2 1.0 4.3 \n", - " 1901 2 1.0 4.3 \n", - " 1902 2 1.0 4.3 \n", - "... ... ... ... \n", - "e999 1905 2 1.0 4.3 \n", - " 1906 2 1.0 4.3 \n", - " 1907 2 1.0 4.3 \n", - "\n", - "[8000 rows x 10 columns]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "panel_data = simulate_data(intensity_by=2, samples=3) # generate data\n", - "\n", - "panel_data" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "811601d0", - "metadata": {}, - "outputs": [], - "source": [ - "att_gt = ATTgt(data=panel_data, cohort_name=\"cohort\", strata_name=\"strata\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "59d747a0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Computing ATTgt for samples = 2 [workers=1]100%|████████████████████| 42/42 [00:00<00:00, 92.14it/s]\n", - "Computing ATTgt for samples = 0 [workers=1]100%|████████████████████| 42/42 [00:00<00:00, 95.82it/s]\n", - "Computing ATTgt for samples = 1 [workers=1]100%|████████████████████| 42/42 [00:00<00:00, 95.95it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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ATTgtResult
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
sample_namestratumcohortbase_periodtime
samples = 20190119001901-2.3131732.816396-7.8332083.206863
19023.3246372.912590-2.3839339.033208
190311.3789222.7779835.93417616.823669*
..............................
samples = 111906190419052.9832482.933005-2.7653368.731831
19051906-0.3342972.554715-5.3414474.672852
19070.3439713.023992-5.5829446.270887
\n", - "

126 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " ATTgtResult \\\n", - " analytic \n", - " ATT std_error \n", - "sample_name stratum cohort base_period time \n", - "samples = 2 0 1901 1900 1901 -2.313173 2.816396 \n", - " 1902 3.324637 2.912590 \n", - " 1903 11.378922 2.777983 \n", - "... ... ... \n", - "samples = 1 1 1906 1904 1905 2.983248 2.933005 \n", - " 1905 1906 -0.334297 2.554715 \n", - " 1907 0.343971 3.023992 \n", - "\n", - " \\\n", - " pointwise conf. band \n", - " lower upper \n", - "sample_name stratum cohort base_period time \n", - "samples = 2 0 1901 1900 1901 -7.833208 3.206863 \n", - " 1902 -2.383933 9.033208 \n", - " 1903 5.934176 16.823669 \n", - "... ... ... \n", - "samples = 1 1 1906 1904 1905 -2.765336 8.731831 \n", - " 1905 1906 -5.341447 4.672852 \n", - " 1907 -5.582944 6.270887 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "sample_name stratum cohort base_period time \n", - "samples = 2 0 1901 1900 1901 \n", - " 1902 \n", - " 1903 * \n", - "... ... \n", - "samples = 1 1 1906 1904 1905 \n", - " 1905 1906 \n", - " 1907 \n", - "\n", - "[126 rows x 5 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.fit(formula=\"y\", split_sample_by=\"samples\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3a0da5ae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.ConcatChart(...)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"time\")\n", - "\n", - "att_gt.plot(\n", - " \"time\",\n", - " configure_header={\"titleFontSize\": 18, \"labelFontSize\": 15},\n", - " estimation_details=False,\n", - " configure_axisX={\"format\": \"c\"},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "c9c7c0a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DifferenceTimeAggregation
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
sample_namedifference_betweentime
samples = 20 - 11901-0.3667492.745978-5.7487675.015269
19020.6892423.101619-5.3898196.768304
19031.0077732.002671-2.9173894.932936
........................
samples = 10 - 119055.3208882.2121450.9851629.656613*
19064.6428671.7516821.2096338.076101*
19076.2230821.9084722.4825459.963619*
\n", - "

21 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " DifferenceTimeAggregation \\\n", - " analytic \n", - " ATT std_error \n", - "sample_name difference_between time \n", - "samples = 2 0 - 1 1901 -0.366749 2.745978 \n", - " 1902 0.689242 3.101619 \n", - " 1903 1.007773 2.002671 \n", - "... ... ... \n", - "samples = 1 0 - 1 1905 5.320888 2.212145 \n", - " 1906 4.642867 1.751682 \n", - " 1907 6.223082 1.908472 \n", - "\n", - " \\\n", - " pointwise conf. band \n", - " lower upper \n", - "sample_name difference_between time \n", - "samples = 2 0 - 1 1901 -5.748767 5.015269 \n", - " 1902 -5.389819 6.768304 \n", - " 1903 -2.917389 4.932936 \n", - "... ... ... \n", - "samples = 1 0 - 1 1905 0.985162 9.656613 \n", - " 1906 1.209633 8.076101 \n", - " 1907 2.482545 9.963619 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "sample_name difference_between time \n", - "samples = 2 0 - 1 1901 \n", - " 1902 \n", - " 1903 \n", - "... ... \n", - "samples = 1 0 - 1 1905 * \n", - " 1906 * \n", - " 1907 * \n", - "\n", - "[21 rows x 5 columns]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"time\", difference=[0, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "50400524", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['samples = 2', 'samples = 0', 'samples = 1']" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.sample_names" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "c3bb845e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DifferenceTimeAggregation
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
stratumdifference_betweentime
0samples = 2 - samples = 019011.4707212.102341-2.6497935.591234
1902-10.0069372.011322-13.949056-6.064819*
1903-6.4498231.612146-9.609571-3.290075*
........................
1samples = 2 - samples = 01905-8.0458951.515554-11.016327-5.075464*
1906-4.2076331.397745-6.947163-1.468103*
1907-4.7281021.423883-7.518862-1.937342*
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14 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " DifferenceTimeAggregation \\\n", - " analytic \n", - " ATT std_error \n", - "stratum difference_between time \n", - "0 samples = 2 - samples = 0 1901 1.470721 2.102341 \n", - " 1902 -10.006937 2.011322 \n", - " 1903 -6.449823 1.612146 \n", - "... ... ... \n", - "1 samples = 2 - samples = 0 1905 -8.045895 1.515554 \n", - " 1906 -4.207633 1.397745 \n", - " 1907 -4.728102 1.423883 \n", - "\n", - " \\\n", - " pointwise conf. band \n", - " lower upper \n", - "stratum difference_between time \n", - "0 samples = 2 - samples = 0 1901 -2.649793 5.591234 \n", - " 1902 -13.949056 -6.064819 \n", - " 1903 -9.609571 -3.290075 \n", - "... ... ... \n", - "1 samples = 2 - samples = 0 1905 -11.016327 -5.075464 \n", - " 1906 -6.947163 -1.468103 \n", - " 1907 -7.518862 -1.937342 \n", - "\n", - " \n", - " \n", - " zero_not_in_cband \n", - "stratum difference_between time \n", - "0 samples = 2 - samples = 0 1901 \n", - " 1902 * \n", - " 1903 * \n", - "... ... \n", - "1 samples = 2 - samples = 0 1905 * \n", - " 1906 * \n", - " 1907 * \n", - "\n", - "[14 rows x 5 columns]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "att_gt.aggregate(\"time\", difference=[\"samples = 2\", \"samples = 0\"])" - ] - }, - { - "cell_type": "markdown", - "id": "3bd98f91", - "metadata": {}, - "source": [ - "## Custom Estimation & DoubleML" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "1d89acae", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", - "\n", - "# outcome model\n", - "outcome_model = RandomForestRegressor(n_estimators=100, max_depth=2)\n", - "\n", - "# propensity score model\n", - "pscore_model = RandomForestClassifier(n_estimators=100, max_depth=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "f6e129a9", - "metadata": {}, - "outputs": [], - "source": [ - "from functools import partial\n", - "from differences.did.double_ml import aiptw_double_ml_did_panel\n", - "\n", - "# use partial to set those models for cross-fitting\n", - "\n", - "aiptw = partial(\n", - " aiptw_double_ml_did_panel,\n", - " pscore_model=pscore_model,\n", - " outcome_model=outcome_model,\n", - " outcome_type=\"continuous\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "48d94db4", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Computing ATTgt [workers=10] 100%|████████████████████| 42/42 [00:27<00:00, 1.53it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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ATTgtResult
analyticpointwise conf. band
ATTstd_errorlowerupperzero_not_in_cband
stratumcohortbase_periodtime
0190119001901-2.8093431.806964-6.3509270.732242
19025.3002361.7240921.9210778.679394*
190310.9817191.7595777.53301214.430427*
...........................
1190619041905-2.1602581.731272-5.5534881.232972
190519060.0151551.767796-3.4496613.479971
19074.8124291.7225231.4363468.188512*
\n", - "

42 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " ATTgtResult \\\n", - " analytic pointwise conf. band \n", - " ATT std_error lower \n", - "stratum cohort base_period time \n", - "0 1901 1900 1901 -2.809343 1.806964 -6.350927 \n", - " 1902 5.300236 1.724092 1.921077 \n", - " 1903 10.981719 1.759577 7.533012 \n", - "... ... ... ... \n", - "1 1906 1904 1905 -2.160258 1.731272 -5.553488 \n", - " 1905 1906 0.015155 1.767796 -3.449661 \n", - " 1907 4.812429 1.722523 1.436346 \n", - "\n", - " \n", - " \n", - " upper zero_not_in_cband \n", - "stratum cohort base_period time \n", - "0 1901 1900 1901 0.732242 \n", - " 1902 8.679394 * \n", - " 1903 14.430427 * \n", - "... ... ... \n", - "1 1906 1904 1905 1.232972 \n", - " 1905 1906 3.479971 \n", - " 1907 8.188512 * \n", - "\n", - "[42 rows x 5 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now, instead of selecting from the menu of options available for the est_method,\n", - "# we plug in the function we composed above\n", - "\n", - "att_gt.fit(\n", - " formula=\"y ~ x0\",\n", - " n_jobs=-1,\n", - " est_method=aiptw, # <--- plug in here\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/doc/source/notebooks/twfe.ipynb b/doc/source/notebooks/twfe.ipynb deleted file mode 100644 index d40400b..0000000 --- a/doc/source/notebooks/twfe.ipynb +++ /dev/null @@ -1,554 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TWFE" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from differences import simulate_data, TWFE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "see [plotto](https://plotto.readthedocs.io/en/latest/notebooks/plotto.html#Plot-parameters) to configure the plots. the .plot() method takes the same arguments as the function [mark_plot() in plotto](https://plotto.readthedocs.io/en/latest/api_reference/plotto.html#plotto.plotto.mark_plot)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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yx0wcat.0cat.1effectcohortintensity
entitytime
e019000.3763430.1602453.729827000.0NaNNaN
19011.664105-1.9334041.691201000.0NaNNaN
1902-11.229124-0.0568131.786326000.0NaNNaN
19030.5424250.8101181.741609000.0NaNNaN
19041.1112930.1012050.806004000.0NaNNaN
..............................
e9991903-12.9163051.3550230.688025000.0NaNNaN
1904-7.780740-0.3812671.952084000.0NaNNaN
1905-16.677634-0.2586041.801049000.0NaNNaN
19060.5563901.5045640.616657000.0NaNNaN
1907-4.1536300.4727400.674045000.0NaNNaN
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\n", - "
" - ], - "text/plain": [ - " y x0 w cat.0 cat.1 effect cohort \\\n", - "entity time \n", - "e0 1900 0.376343 0.160245 3.729827 0 0 0.0 NaN \n", - " 1901 1.664105 -1.933404 1.691201 0 0 0.0 NaN \n", - " 1902 -11.229124 -0.056813 1.786326 0 0 0.0 NaN \n", - " 1903 0.542425 0.810118 1.741609 0 0 0.0 NaN \n", - " 1904 1.111293 0.101205 0.806004 0 0 0.0 NaN \n", - "... ... ... ... ... ... ... ... \n", - "e999 1903 -12.916305 1.355023 0.688025 0 0 0.0 NaN \n", - " 1904 -7.780740 -0.381267 1.952084 0 0 0.0 NaN \n", - " 1905 -16.677634 -0.258604 1.801049 0 0 0.0 NaN \n", - " 1906 0.556390 1.504564 0.616657 0 0 0.0 NaN \n", - " 1907 -4.153630 0.472740 0.674045 0 0 0.0 NaN \n", - "\n", - " intensity \n", - "entity time \n", - "e0 1900 NaN \n", - " 1901 NaN \n", - " 1902 NaN \n", - " 1903 NaN \n", - " 1904 NaN \n", - "... ... \n", - "e999 1903 NaN \n", - " 1904 NaN \n", - " 1905 NaN \n", - " 1906 NaN \n", - " 1907 NaN \n", - "\n", - "[8000 rows x 8 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "panel_data = simulate_data() # generate data\n", - "\n", - "panel_data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "twfe = TWFE(\n", - " data=panel_data,\n", - " cohort_name=\"cohort\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterstd_errort_statp_valuelowerupper
Intercept0.1701760.4833070.3521077.247582e-01-0.7770881.117439
-63.9436231.1009683.5819613.410253e-041.7857666.101481
-53.3227521.0257073.2394761.197494e-031.3124045.333100
-42.1694480.9016652.4060471.612621e-020.4022173.936679
-30.7997850.7366291.0857372.775954e-01-0.6439812.243552
-22.2235130.8557072.5984529.364502e-030.5463593.900667
0-0.1207400.881288-0.1370048.910277e-01-1.8480321.606552
15.2910890.7083037.4700908.015810e-143.9028406.679338
212.6059981.03153412.2206300.000000e+0010.58422814.627768
321.4530050.87331424.5650500.000000e+0019.74134123.164670
434.6196231.32140326.1991320.000000e+0032.02972037.209526
543.4106811.20799435.9361720.000000e+0041.04305745.778306
654.6681451.47844236.9768590.000000e+0051.77045157.565838
767.2295641.35723449.5342600.000000e+0064.56943569.889693
\n", - "
" - ], - "text/plain": [ - " parameter std_error t_stat p_value lower upper\n", - "Intercept 0.170176 0.483307 0.352107 7.247582e-01 -0.777088 1.117439\n", - "-6 3.943623 1.100968 3.581961 3.410253e-04 1.785766 6.101481\n", - "-5 3.322752 1.025707 3.239476 1.197494e-03 1.312404 5.333100\n", - "-4 2.169448 0.901665 2.406047 1.612621e-02 0.402217 3.936679\n", - "-3 0.799785 0.736629 1.085737 2.775954e-01 -0.643981 2.243552\n", - "-2 2.223513 0.855707 2.598452 9.364502e-03 0.546359 3.900667\n", - "0 -0.120740 0.881288 -0.137004 8.910277e-01 -1.848032 1.606552\n", - "1 5.291089 0.708303 7.470090 8.015810e-14 3.902840 6.679338\n", - "2 12.605998 1.031534 12.220630 0.000000e+00 10.584228 14.627768\n", - "3 21.453005 0.873314 24.565050 0.000000e+00 19.741341 23.164670\n", - "4 34.619623 1.321403 26.199132 0.000000e+00 32.029720 37.209526\n", - "5 43.410681 1.207994 35.936172 0.000000e+00 41.043057 45.778306\n", - "6 54.668145 1.478442 36.976859 0.000000e+00 51.770451 57.565838\n", - "7 67.229564 1.357234 49.534260 0.000000e+00 64.569435 69.889693" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "twfe.fit(\n", - " formula=\"y\",\n", - " cluster_names=\"entity\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "twfe.plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/twfe.ipynb b/examples/twfe.ipynb deleted file mode 100644 index 40d4ad7..0000000 --- a/examples/twfe.ipynb +++ /dev/null @@ -1,554 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TWFE" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from differences import simulate_data, TWFE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "see [plotto](https://plotto.readthedocs.io/en/latest/notebooks/plotto.html#Plot-parameters) to configure the plots. the .plot() method takes the same arguments as the function [mark_plot() in plotto](https://plotto.readthedocs.io/en/latest/api_reference/plotto.html#plotto.plotto.mark_plot)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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yx0wcat.0cat.1effectcohortintensity
entitytime
e019003.5758800.1602453.729827000.001901.00.50
19013.608329-1.9334041.691201000.001901.00.50
19025.132523-0.0568131.786326000.501901.00.50
1903-6.3162400.8101181.741609001.001901.00.50
19043.4095240.1012050.806004001.501901.00.50
..............................
e999190311.3761111.3550230.6880250015.751900.05.25
190411.727819-0.3812671.9520840021.001900.05.25
190519.238415-0.2586041.8010490026.251900.05.25
190640.1455411.5045640.6166570031.501900.05.25
190770.6739520.4727400.6740450036.751900.05.25
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8000 rows × 8 columns

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" - ], - "text/plain": [ - " y x0 w cat.0 cat.1 effect cohort \\\n", - "entity time \n", - "e0 1900 3.575880 0.160245 3.729827 0 0 0.00 1901.0 \n", - " 1901 3.608329 -1.933404 1.691201 0 0 0.00 1901.0 \n", - " 1902 5.132523 -0.056813 1.786326 0 0 0.50 1901.0 \n", - " 1903 -6.316240 0.810118 1.741609 0 0 1.00 1901.0 \n", - " 1904 3.409524 0.101205 0.806004 0 0 1.50 1901.0 \n", - "... ... ... ... ... ... ... ... \n", - "e999 1903 11.376111 1.355023 0.688025 0 0 15.75 1900.0 \n", - " 1904 11.727819 -0.381267 1.952084 0 0 21.00 1900.0 \n", - " 1905 19.238415 -0.258604 1.801049 0 0 26.25 1900.0 \n", - " 1906 40.145541 1.504564 0.616657 0 0 31.50 1900.0 \n", - " 1907 70.673952 0.472740 0.674045 0 0 36.75 1900.0 \n", - "\n", - " intensity \n", - "entity time \n", - "e0 1900 0.50 \n", - " 1901 0.50 \n", - " 1902 0.50 \n", - " 1903 0.50 \n", - " 1904 0.50 \n", - "... ... \n", - "e999 1903 5.25 \n", - " 1904 5.25 \n", - " 1905 5.25 \n", - " 1906 5.25 \n", - " 1907 5.25 \n", - "\n", - "[8000 rows x 8 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "panel_data = simulate_data() # generate data\n", - "\n", - "panel_data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "twfe = TWFE(\n", - " data=panel_data,\n", - " cohort_name=\"cohort\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterstd_errort_statp_valuelowerupper
Intercept3.3930590.5228306.4897988.595147e-112.3683324.417787
-6-2.1549070.972402-2.2160672.668692e-02-4.060780-0.249035
-5-4.0803471.082333-3.7699531.632780e-04-6.201681-1.959012
-4-3.9722201.106781-3.5889853.319678e-04-6.141471-1.802969
-3-6.2891771.103977-5.6968391.220494e-08-8.452931-4.125422
-2-6.4908230.965235-6.7246011.760747e-11-8.382650-4.598997
0-4.1558300.684287-6.0732281.253646e-09-5.497007-2.814652
10.9954930.8730861.1402002.542031e-01-0.7157252.706710
20.3456421.0166290.3399887.338657e-01-1.6469152.338199
31.8596291.0321201.8017567.158380e-02-0.1632903.882548
46.8998931.0003316.8976125.288436e-124.9392818.860504
59.5780891.0670228.9764680.000000e+007.48676411.669413
613.3823291.20379411.1167910.000000e+0011.02293615.741723
727.0399971.36512319.8077400.000000e+0024.36440629.715589
\n", - "
" - ], - "text/plain": [ - " parameter std_error t_stat p_value lower upper\n", - "Intercept 3.393059 0.522830 6.489798 8.595147e-11 2.368332 4.417787\n", - "-6 -2.154907 0.972402 -2.216067 2.668692e-02 -4.060780 -0.249035\n", - "-5 -4.080347 1.082333 -3.769953 1.632780e-04 -6.201681 -1.959012\n", - "-4 -3.972220 1.106781 -3.588985 3.319678e-04 -6.141471 -1.802969\n", - "-3 -6.289177 1.103977 -5.696839 1.220494e-08 -8.452931 -4.125422\n", - "-2 -6.490823 0.965235 -6.724601 1.760747e-11 -8.382650 -4.598997\n", - "0 -4.155830 0.684287 -6.073228 1.253646e-09 -5.497007 -2.814652\n", - "1 0.995493 0.873086 1.140200 2.542031e-01 -0.715725 2.706710\n", - "2 0.345642 1.016629 0.339988 7.338657e-01 -1.646915 2.338199\n", - "3 1.859629 1.032120 1.801756 7.158380e-02 -0.163290 3.882548\n", - "4 6.899893 1.000331 6.897612 5.288436e-12 4.939281 8.860504\n", - "5 9.578089 1.067022 8.976468 0.000000e+00 7.486764 11.669413\n", - "6 13.382329 1.203794 11.116791 0.000000e+00 11.022936 15.741723\n", - "7 27.039997 1.365123 19.807740 0.000000e+00 24.364406 29.715589" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "twfe.fit(\n", - " formula=\"y\",\n", - " cluster_names=\"entity\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "twfe.plot()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "test-differences-py38", - "language": "python", - "name": "test-differences-py38" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/attgt.ipynb b/notebooks/v0-1/attgt.ipynb similarity index 100% rename from examples/attgt.ipynb rename to notebooks/v0-1/attgt.ipynb diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 3ef1f9f..0000000 --- a/poetry.lock +++ /dev/null @@ -1,3107 +0,0 @@ -[[package]] -name = "alabaster" -version = "0.7.13" -description = "A configurable sidebar-enabled Sphinx theme" -category = "dev" -optional = false -python-versions = ">=3.6" - 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a/differences/__init__.py b/src/differences/__init__.py similarity index 100% rename from differences/__init__.py rename to src/differences/__init__.py diff --git a/differences/attgt/__init__.py b/src/differences/attgt/__init__.py similarity index 100% rename from differences/attgt/__init__.py rename to src/differences/attgt/__init__.py diff --git a/differences/attgt/aggregate.py b/src/differences/attgt/aggregate.py similarity index 100% rename from differences/attgt/aggregate.py rename to src/differences/attgt/aggregate.py diff --git a/differences/attgt/attgt.py b/src/differences/attgt/attgt.py similarity index 100% rename from differences/attgt/attgt.py rename to src/differences/attgt/attgt.py diff --git a/differences/attgt/attgt_cal.py b/src/differences/attgt/attgt_cal.py similarity index 100% rename from differences/attgt/attgt_cal.py rename to src/differences/attgt/attgt_cal.py diff --git a/differences/attgt/difference.py b/src/differences/attgt/difference.py similarity index 100% rename from differences/attgt/difference.py rename to src/differences/attgt/difference.py diff --git a/differences/attgt/mboot.py b/src/differences/attgt/mboot.py similarity index 100% rename from differences/attgt/mboot.py rename to src/differences/attgt/mboot.py diff --git a/differences/attgt/plot.py b/src/differences/attgt/plot.py similarity index 100% rename from differences/attgt/plot.py rename to src/differences/attgt/plot.py diff --git a/differences/attgt/utility.py b/src/differences/attgt/utility.py similarity index 100% rename from differences/attgt/utility.py rename to src/differences/attgt/utility.py diff --git a/differences/datasets/__init__.py b/src/differences/datasets/__init__.py similarity index 100% rename from differences/datasets/__init__.py rename to src/differences/datasets/__init__.py diff --git a/differences/datasets/generate_data.py b/src/differences/datasets/generate_data.py similarity index 100% rename from differences/datasets/generate_data.py rename to src/differences/datasets/generate_data.py diff --git a/differences/datasets/load_data.py b/src/differences/datasets/load_data.py similarity index 100% rename from differences/datasets/load_data.py rename to src/differences/datasets/load_data.py diff --git a/differences/datasets/me.csv b/src/differences/datasets/me.csv similarity index 100% rename from differences/datasets/me.csv rename to src/differences/datasets/me.csv diff --git a/differences/datasets/mpdta.csv b/src/differences/datasets/mpdta.csv similarity index 100% rename from differences/datasets/mpdta.csv rename to src/differences/datasets/mpdta.csv diff --git a/differences/datasets/rcs-multi_period.csv b/src/differences/datasets/rcs-multi_period.csv similarity index 100% rename from differences/datasets/rcs-multi_period.csv rename to src/differences/datasets/rcs-multi_period.csv diff --git a/differences/datasets/rcs-two_periods.csv b/src/differences/datasets/rcs-two_periods.csv similarity index 100% rename from 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a/differences/did/pscore_cal.py b/src/differences/did/pscore_cal.py similarity index 100% rename from differences/did/pscore_cal.py rename to src/differences/did/pscore_cal.py diff --git a/differences/did/utility.py b/src/differences/did/utility.py similarity index 100% rename from differences/did/utility.py rename to src/differences/did/utility.py diff --git a/differences/tests/__init__.py b/src/differences/tools/__init__.py similarity index 100% rename from differences/tests/__init__.py rename to src/differences/tools/__init__.py diff --git a/differences/tools/__init__.py b/src/differences/tools/feols/__init__.py similarity index 100% rename from differences/tools/__init__.py rename to src/differences/tools/feols/__init__.py diff --git a/differences/tools/feols/ols.py b/src/differences/tools/feols/ols.py similarity index 100% rename from differences/tools/feols/ols.py rename to src/differences/tools/feols/ols.py diff --git a/differences/tools/feols/utility.py b/src/differences/tools/feols/utility.py similarity index 100% rename from differences/tools/feols/utility.py rename to src/differences/tools/feols/utility.py diff --git a/differences/tools/panel_utility.py b/src/differences/tools/panel_utility.py similarity index 100% rename from differences/tools/panel_utility.py rename to src/differences/tools/panel_utility.py diff --git a/differences/tools/panel_validation.py b/src/differences/tools/panel_validation.py similarity index 100% rename from differences/tools/panel_validation.py rename to src/differences/tools/panel_validation.py diff --git a/differences/tools/relative_periods.py b/src/differences/tools/relative_periods.py similarity index 100% rename from differences/tools/relative_periods.py rename to src/differences/tools/relative_periods.py diff --git a/differences/tools/utility.py b/src/differences/tools/utility.py similarity index 100% rename from differences/tools/utility.py rename to src/differences/tools/utility.py diff --git a/differences/twfe/__init__.py b/src/differences/twfe/__init__.py similarity index 100% rename from differences/twfe/__init__.py rename to src/differences/twfe/__init__.py diff --git a/differences/twfe/plot.py b/src/differences/twfe/plot.py similarity index 100% rename from differences/twfe/plot.py rename to src/differences/twfe/plot.py diff --git a/differences/twfe/twfe.py b/src/differences/twfe/twfe.py similarity index 100% rename from differences/twfe/twfe.py rename to src/differences/twfe/twfe.py diff --git a/differences/twfe/utility.py b/src/differences/twfe/utility.py similarity index 100% rename from differences/twfe/utility.py rename to src/differences/twfe/utility.py diff --git a/tox.ini b/tox.ini deleted file mode 100644 index 1faebe2..0000000 --- a/tox.ini +++ /dev/null @@ -1,9 +0,0 @@ -[tox] -isolated_build = True -envlist = py38,py39,py310,py311 - -[testenv] -deps = - -r requirements-dev.txt -commands = - pytest differences/tests