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.
This tool helps you understand how different interventions or 'treatments' causally impact an outcome, especially when that impact varies across different groups of people or situations. You provide your observational data, including details about the treatment, the outcome, and other influencing factors, and it outputs an estimate of the causal effect and how it changes based on specific characteristics. This is ideal for economists, marketers, policy analysts, and data scientists looking to make personalized decisions.
4,537 stars. Used by 2 other packages. Actively maintained with 7 commits in the last 30 days. Available on PyPI.
Use this if you need to measure the causal impact of a specific action or policy from historical or non-experimental data, and you want to understand how that impact differs across various segments of your audience.
Not ideal if your primary goal is simple prediction without needing to understand the underlying cause-and-effect relationships.
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Last pushed
Mar 09, 2026
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