uber/causalml

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

5973 stars · 874 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

library · maturity stable

machine-learning data-science machine-learning data-science analytics python causal-inference uplift-modeling treatment-effect cate a-b-testing marketing-analytics

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markdown · JSON · MCP: product_card(name="uber/causalml")