A pre-cluster and merge algorithm provably recovers the number of hidden effect levels, the average treatment effect per level, and per-subject level membership in non-targeted trials.
EconML : A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
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Consistent Causal Inference of Group Effects in Non-Targeted Trials with Finitely Many Effect Levels
A pre-cluster and merge algorithm provably recovers the number of hidden effect levels, the average treatment effect per level, and per-subject level membership in non-targeted trials.