py-why/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

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The package supports heterogeneous treatment effect estimation across multiple methodologies including instrumental variable approaches and methods robust to unobserved confounding, each with asymptotically valid confidence intervals. Beyond estimation, it provides interpretability tools, causal model selection via cross-validation, and policy learning capabilities to optimize treatment assignment decisions. Built on scikit-learn conventions, it integrates seamlessly with standard ML workflows while maintaining econometric rigor for valid statistical inference on observational data.

4,537 stars and 550,132 monthly downloads. Used by 2 other packages. Available on PyPI.

Maintenance 13 / 25
Adoption 22 / 25
Maturity 25 / 25
Community 24 / 25

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Stars

4,537

Forks

798

Language

Jupyter Notebook

License

Last pushed

Mar 09, 2026

Monthly downloads

550,132

Commits (30d)

0

Dependencies

10

Reverse dependents

2

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