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. observed · 2026-08-28
Health v2 · maintenance only
87/100
- Activity 99
- Release rhythm 64
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 386
- age_days: 3047
- days_rel: 29
- days_push: 9
- n_releases_24m: 2
Adoption not part of the score
4763 stars · 823 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
EconML is a Python package from Microsoft Research's ALICE project for estimating heterogeneous treatment effects from observational data using machine learning. It combines state-of-the-art ML techniques with econometrics, implementing methods like double machine learning with a unified scikit-learn-style API and confidence interval support.
Use cases
- estimate causal effect of a treatment on an outcome from observational data
- measure heterogeneous treatment effects across subpopulations
- run double machine learning for causal inference in Python
- estimate conditional average treatment effects (CATE)
- do uplift modeling for personalized decision making
- perform causal analysis with instrumental variables
- get confidence intervals for estimated causal effects
When to choose
- you need to estimate how a treatment's causal effect varies with features using observational data
- you want econometrically valid causal estimates with confidence intervals while using flexible ML models
- you need double machine learning, orthogonal ML, or IV-based causal estimation methods in Python
When to avoid
- you only need predictive modeling without causal interpretation
- your data has strong unobserved confounding and no instrument is available
- you need full causal graph discovery rather than treatment effect estimation
Facets
library · maturity active
machine-learning data-science sdk machine-learning data-science python cross-platform causal-inference econometrics treatment-effects double-machine-learning heterogeneous-treatment-effects observational-data economics algorithms
6 sources
- readme: https://github.com/py-why/EconML · fetched 2026-08-28 · 323b487583b4
- homepage: https://www.microsoft.com/en-us/research/project/alice/ · fetched 2026-08-29 · 7892c4d93592
- site_page: https://www.microsoft.com/en-us/research/about-microsoft-research · fetched 2026-08-29 · 662089aedfa3
- site_page: https://learn.microsoft.com/docs · fetched 2026-08-29 · f29800be2b9a
- site_page: https://www.microsoft.com/about · fetched 2026-08-29 · a6c7cec3c6d7
- registry_pypi: https://pypi.org/pypi/econml/json · fetched 2026-08-29 · 4e85176791c7
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| py-why/EconML | main | 87 |
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