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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