# 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.

Repository: https://github.com/py-why/EconML
Canonical: https://ross.abutalabs.com/products/econml
Homepage: https://www.microsoft.com/en-us/research/project/alice/
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: machine-learning, economics, causal-inference, causality, econometrics, treatment-effects
Last push: 2026-08-24T18:49:28+00:00

## Health v2 (maintenance only)
Score: 87/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 64, longevity 100
- inputs: {"age_days": 3047, "days_push": 9, "days_rel": 29, "gap_med": 386, "n_releases_24m": 2}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4763, forks 823 (observed 2026-08-28T04:08:59.134556+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: machine-learning, data-science, sdk
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: causal-inference, econometrics, treatment-effects, double-machine-learning, heterogeneous-treatment-effects, observational-data, economics, algorithms

## Member repositories
- py-why/EconML (main) score 87

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:59.134556+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:18:51.177698+00:00, confidence not recorded.
  - readme: https://github.com/py-why/EconML (fetched 2026-08-28T04:08:59.134556+00:00, sha 323b487583b4)
  - homepage: https://www.microsoft.com/en-us/research/project/alice/ (fetched 2026-08-29T09:02:26.242850+00:00, sha 7892c4d93592)
  - site_page: https://www.microsoft.com/en-us/research/about-microsoft-research (fetched 2026-08-29T09:02:26.252071+00:00, sha 662089aedfa3)
  - site_page: https://learn.microsoft.com/docs (fetched 2026-08-29T09:02:26.254183+00:00, sha f29800be2b9a)
  - site_page: https://www.microsoft.com/about (fetched 2026-08-29T09:02:26.255989+00:00, sha a6c7cec3c6d7)
  - registry_pypi: https://pypi.org/pypi/econml/json (fetched 2026-08-29T09:02:26.259603+00:00, sha 4e85176791c7)
- Data as of 2026-08-30T08:39:29.467469+00:00.