Directory

Python causal-inference library organized around model, identify, estimate and refute steps, with graphical causal models for attribution, counterfactuals and root-cause analysis

Python library for heterogeneous treatment-effect estimation with machine learning: double ML, doubly robust learners, causal forests, meta-learners, IV and policy learning

R package for fast estimation of OLS, GLM, Poisson, IV and difference-in-differences models with many fixed effects, clustered errors, multiple estimations and publication tables