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Minor fixes #891

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merged 6 commits into from
Jul 3, 2024
Merged

Minor fixes #891

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dac7b4b
Add blog reference to README
kbattocchi Jun 10, 2024
8dcdbb5
Set n_estimators correctly for DRPolicyForest
kbattocchi Jun 10, 2024
b20fcf2
Correct minor CausalForestDML documenation issue
kbattocchi Mar 21, 2024
4fbd10e
Add "help wanted" content to README
kbattocchi Jun 26, 2024
5a3dbf0
Enable support for scikit-learn 1.5
kbattocchi Jun 27, 2024
3dff057
Prevent use of numpy 2.0
kbattocchi Jun 28, 2024
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -3,8 +3,6 @@
[![PyPI wheel](https://img.shields.io/pypi/wheel/econml.svg)](https://pypi.org/project/econml/)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/econml.svg)](https://pypi.org/project/econml/)



<h1>
<a href="https://econml.azurewebsites.net/">
<img src="doc/econml-logo-icon.png" width="80px" align="left" style="margin-right: 10px;", alt="econml-logo">
Expand Down Expand Up @@ -691,6 +689,8 @@ We use GitHub Actions to build and publish the package and documentation. To cr

# Blogs and Publications

* May 2021: [Be Careful When Interpreting Predictive Models in Search of Causal Insights](https://towardsdatascience.com/be-careful-when-interpreting-predictive-models-in-search-of-causal-insights-e68626e664b6)

* June 2019: [Treatment Effects with Instruments paper](https://arxiv.org/pdf/1905.10176.pdf)

* May 2019: [Open Data Science Conference Workshop](https://odsc.com/speakers/machine-learning-estimation-of-heterogeneous-treatment-effect-the-microsoft-econml-library/)
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3 changes: 2 additions & 1 deletion econml/policy/_drlearner.py
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Expand Up @@ -854,7 +854,8 @@ def _gen_drpolicy_learner(self):
cv=self.cv,
mc_iters=self.mc_iters,
mc_agg=self.mc_agg,
model_final=PolicyForest(max_depth=self.max_depth,
model_final=PolicyForest(n_estimators=self.n_estimators,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
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4 changes: 2 additions & 2 deletions econml/tests/test_policy_forest.py
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Expand Up @@ -321,8 +321,8 @@ def test_non_standard_input(self,):
model_propensity=DummyClassifier(strategy='uniform'),
featurizer=PolynomialFeatures(degree=1, include_bias=False),
cv=GroupKFold(n_splits=2),
n_estimators=20, n_jobs=1, random_state=123).fit(y, T, X=X,
groups=groups)
n_estimators=100, n_jobs=1, random_state=123).fit(y, T, X=X,
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Is this for test stability?

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Because n_estimators was never passed correctly to DRPolicyForest in the past, this has always effectively run with n_estimators=100 (the default value), and the tests don't pass if we actually pass 20 through instead.

groups=groups)
mask = np.abs(Xraw[:, 0]) > .1
np.testing.assert_allclose(pred[mask], forest.predict(Xraw[mask]))
np.testing.assert_allclose(pred_val[mask, 1] - pred_val[mask, 0],
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