Fix build issues

azure-pipelines.yml

@@ -10,7 +10,7 @@ jobs:
 - job: 'EvalChanges'
   displayName: 'Analyze changed files to determine which job to run'
   pool:
-    vmImage: 'macOS-10.13'
+    vmImage: 'macOS-10.15'
   steps:
   # We want to enforce the following rules for PRs:
   # * if all modifications are to README.md
@@ -144,7 +144,7 @@ jobs:
   variables:
     python.version: '3.6'
   pool:
-    vmImage: 'macOS-10.13'
+    vmImage: 'macOS-10.15'
   steps:
   - template: azure-pipelines-steps.yml
     parameters:
@@ -162,7 +162,7 @@ jobs:
         imageName: 'ubuntu-16.04'
         python.version: '3.5'
       macOS, Python 3.5:
-        imageName: 'macOS-10.13'
+        imageName: 'macOS-10.15'
         python.version: '3.5'
       Windows, Python 3.5:
         imageName: 'vs2017-win2016'
@@ -171,7 +171,7 @@ jobs:
         imageName: 'ubuntu-16.04'
         python.version: '3.6'
       macOS, Python 3.6:
-        imageName: 'macOS-10.13'
+        imageName: 'macOS-10.15'
         python.version: '3.6'
       Windows, Python 3.6:
         imageName: 'vs2017-win2016'
@@ -180,7 +180,7 @@ jobs:
         imageName: 'ubuntu-16.04'
         python.version: '3.7'
       macOS, Python 3.7:
-        imageName: 'macOS-10.13'
+        imageName: 'macOS-10.15'
         python.version: '3.7'
       Windows, Python 3.7:
         imageName: 'vs2017-win2016'

doc/spec/comparison.rst

@@ -36,60 +36,59 @@ Detailed estimator comparison
 | :class:`.NonParamDMLCateEstimator`          | 1-d/Binary   |              |                  | Yes         |                 | Yes        |              | Yes                |
 +---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
 
-.. glossary::
 
-    Treatment Type
-        Some estimators can only estimate effects of particular kinds of treatments. 
-        *Discrete* treatments can be described by a finite number of comprehensive categories (for example, 
-        group A received a 10% discount on product 1, group B received a 10% discount on product 2, group C 
-        received no discounts). *Binary* treatments are a special case of discrete treatments with only two 
-        categories. *Continuous* treatments can take on any value along the number line (for example, minutes of 
-        exercise per week).  
+Treatment Type
+    Some estimators can only estimate effects of particular kinds of treatments. 
+    *Discrete* treatments can be described by a finite number of comprehensive categories (for example, 
+    group A received a 10% discount on product 1, group B received a 10% discount on product 2, group C 
+    received no discounts). *Binary* treatments are a special case of discrete treatments with only two 
+    categories. *Continuous* treatments can take on any value along the number line (for example, minutes of 
+    exercise per week).  
 
-    Requires Instrument
-        Some estimators identify the causal effect of a treatment by considering only a subset of the variation in 
-        treatment intensity that is conditionally random given other data features. This subset of the variation 
-        is driven by an instrument, which is usually some kind of randomization (i.e. an earlier experiment or a 
-        lottery). See the Instrumental Variable Regression section for more information on picking a good 
-        instrument.  
+Requires Instrument
+    Some estimators identify the causal effect of a treatment by considering only a subset of the variation in 
+    treatment intensity that is conditionally random given other data features. This subset of the variation 
+    is driven by an instrument, which is usually some kind of randomization (i.e. an earlier experiment or a 
+    lottery). See the Instrumental Variable Regression section for more information on picking a good 
+    instrument.  
 
-    Delivers Confidence Intervals
-        Many estimators can deliver analytic confidence intervals for the final treatment effects. These 
-        confidence intervals correctly adjust for the reuse of data across multiple stages of estimation. EconML 
-        cannot deliver analytic confidence intervals in cases where this multi-stage estimation is too complex or 
-        for estimators such as the MetaLearners that trade honest confidence intervals for model selection and 
-        regularization. In these cases it is still possible to get bootstrap confidence intervals, but this 
-        process is slow and may not be statistically valid. 
+Delivers Confidence Intervals
+    Many estimators can deliver analytic confidence intervals for the final treatment effects. These 
+    confidence intervals correctly adjust for the reuse of data across multiple stages of estimation. EconML 
+    cannot deliver analytic confidence intervals in cases where this multi-stage estimation is too complex or 
+    for estimators such as the MetaLearners that trade honest confidence intervals for model selection and 
+    regularization. In these cases it is still possible to get bootstrap confidence intervals, but this 
+    process is slow and may not be statistically valid. 
 
-    Linear Treatment
-        Some estimators impose the assumption that the outcome is a linear function of the treatment. These 
-        estimators can also estimate a non-linear relationship between a treatment and the outcome if the 
-        structure of the relationship is known and additively separable (for example, the linear function could 
-        include both treatment and treatment-squared for continuous treatments). These linear functions can also 
-        include specified interactions between treatments. However, these estimators cannot estimate a fully 
-        flexible non-parametric relationship between treatments and the outcome (for example, the relationship 
-        cannot be modeled by a forest). 
+Linear Treatment
+    Some estimators impose the assumption that the outcome is a linear function of the treatment. These 
+    estimators can also estimate a non-linear relationship between a treatment and the outcome if the 
+    structure of the relationship is known and additively separable (for example, the linear function could 
+    include both treatment and treatment-squared for continuous treatments). These linear functions can also 
+    include specified interactions between treatments. However, these estimators cannot estimate a fully 
+    flexible non-parametric relationship between treatments and the outcome (for example, the relationship 
+    cannot be modeled by a forest). 
 
-    Linear Heterogeneity
-        The CATE function determines how the size of a user’s response to the treatment varies by user features. 
-        Some estimators impose the *assumption* that effect size is a linear function of user features. A few models 
-        estimate a more flexible relationship between effect size and user features and then *project* that flexible
-        function onto a linear model. This second approach delivers a better-fitting linear approximation of a 
-        non-linear relationship, but is less efficient in cases where you are confident assuming the true 
-        relationship is linear. Finally, some estimation models allow a fully flexible relationship between 
-        effect size and user features with no linearity structure. 
+Linear Heterogeneity
+    The CATE function determines how the size of a user’s response to the treatment varies by user features. 
+    Some estimators impose the *assumption* that effect size is a linear function of user features. A few models 
+    estimate a more flexible relationship between effect size and user features and then *project* that flexible
+    function onto a linear model. This second approach delivers a better-fitting linear approximation of a 
+    non-linear relationship, but is less efficient in cases where you are confident assuming the true 
+    relationship is linear. Finally, some estimation models allow a fully flexible relationship between 
+    effect size and user features with no linearity structure. 
 
-    Multiple Outcomes
-        Some estimation models allow joint estimation of the effects of treatment(s) on multiple outcomes. Other 
-        models only accommodate a single outcome. 
+Multiple Outcomes
+    Some estimation models allow joint estimation of the effects of treatment(s) on multiple outcomes. Other 
+    models only accommodate a single outcome. 
 
-    Multiple Treatments
-        Some estimation models allow joint estimation of the effects of multiple treatments on outcome(s). Other 
-        models only accommodate a single treatment. 
+Multiple Treatments
+    Some estimation models allow joint estimation of the effects of multiple treatments on outcome(s). Other 
+    models only accommodate a single treatment. 
 
-    High-Dimensional Features
-        Many estimators only behave well with a small set of specified features, X, that affect the size of a 
-        user’s response to the treatment. If you do not already know which few features might reasonably affect 
-        the user’s response, use one of our sparse estimators that can handle large feature sets and penalize them 
-        to discover the features that are most correlated with treatment effect heterogeneity. 
+High-Dimensional Features
+    Many estimators only behave well with a small set of specified features, X, that affect the size of a 
+    user’s response to the treatment. If you do not already know which few features might reasonably affect 
+    the user’s response, use one of our sparse estimators that can handle large feature sets and penalize them 
+    to discover the features that are most correlated with treatment effect heterogeneity. 
 

setup.cfg

@@ -56,6 +56,7 @@ tests_require =
     pytest-xdist
     pytest-cov
     jupyter
+    nbconvert != 6.0.0a1; python_version <= '3.5'
     seaborn
     lightgbm
     dowhy