# uber/causalml

Uplift modeling and causal inference with machine learning algorithms

Repository: https://github.com/uber/causalml
Canonical: https://ross.abutalabs.com/products/causalml
Language: Python
License: NOASSERTION
License Family: other
Topics: incubation, machine-learning, causal-inference, uplift-modeling
Last push: 2026-08-20T15:25:36+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 67, longevity 100
- inputs: {"age_days": 2613, "days_push": 13, "days_rel": 60, "gap_med": 142, "n_releases_24m": 6}
- 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 5973, forks 874 (observed 2026-08-28T04:09:33.889814+00:00)

## What it is
Causal ML is a Python package providing uplift modeling and causal inference methods built on machine learning algorithms. It estimates the Conditional Average Treatment Effect (CATE) from experimental or observational data through a standard estimator interface.

## Use cases
- estimate heterogeneous treatment effects from A/B test data
- optimize ad campaign targeting by uplift score
- personalize customer engagement and treatment recommendations
- estimate conditional average treatment effect from observational data
- compare uplift modeling estimators like meta-learners and causal forests
- measure incremental impact of marketing interventions on sales

## When to choose
- you need to estimate individual-level causal effects of a treatment or intervention
- you want to target customers by predicted uplift rather than predicted outcome
- you have A/B experiment or observational data with features, treatment, and outcome
- you want a maintained Python library with many CATE estimators behind one interface

## When to avoid
- you need plain supervised prediction without causal interpretation
- you require strict API stability, since some experimental APIs may change
- you need deep-learning-based causal estimation beyond the provided estimator families

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science
- domain: machine-learning, data-science, analytics
- platform: python
- tags: causal-inference, uplift-modeling, treatment-effect, cate, a-b-testing, marketing-analytics

## Member repositories
- uber/causalml (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:33.889814+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 2026-08-29T17:50:08.558522+00:00, confidence not recorded.
  - readme: https://github.com/uber/causalml (fetched 2026-08-28T04:09:33.889814+00:00, sha 7cf4d6987ef2)
  - registry_pypi: https://pypi.org/pypi/causalml/json (fetched 2026-08-29T08:46:00.817383+00:00, sha bfda9ca4ad12)
- Data as of 2026-08-30T08:39:29.467469+00:00.