ml-causal

What problem does it solve?

Estimates causal effects from observational data using ML-based nuisance estimation to combine flexible modeling with rigorous identification.

Core Features & Use Cases

• Double/Debiased Machine Learning (DML) for partially linear models to estimate the causal parameter θ and ATE.
• Causal Forest (GRF) for estimating heterogeneous treatment effects (CATE) and exploring effect heterogeneity.
• BLP (Best Linear Predictor) and CLAN analyses to assess variation in CATE across observables.
• AIPW / DR learners and meta-learners for robust CATE estimation with cross-fitting.

Quick Start

Prepare your data (Y, D, X) and select a method (e.g., DML or causal forest) to estimate treatment effects and CATE.