#hte

10 notes

• CATE (Conditional Average Treatment Effect) The Conditional Average Treatment Effect (CATE) is the average treatment effect given covariates $X=x$:
• Causal Forest Causal Forest is a causal-inference application of the Generalized Random Forest (GRF) proposed by Athey, Tibshirani, and Wager (2019), splitting so as to maximize the heterogeneity of treatment effects.
• DR-Learner The DR-Learner is a two-stage doubly robust estimator for CATE that regresses a pseudo-outcome on the covariates.
• HTE (Heterogeneous Treatment Effects) The phenomenon in which the treatment effect varies with an individual's characteristics
• ITE (Individual Treatment Effect) The treatment effect for individual $i$
• Meta-learners Meta-learners are a general term for algorithms that estimate the CATE by leveraging existing supervised learning methods (base learners).
• R-Learner R-Learner (Residualized Learner) is a meta-learner that estimates the CATE using residualized outcomes and residualized treatments based on the Robinson Transformation.
• S-Learner The S-Learner (Single Learner) is a Meta-learner that estimates the response function with a single model including the treatment indicator as a feature, then computes the CATE.
• T-Learner The T-Learner (Two Learner) is a Meta-learner that estimates the CATE by training separate models for the treatment group and the control group.
• X-Learner The X-Learner is a three-stage algorithm that leverages imputed treatment effects, a meta-learner that effectively exploits group imbalance and the structural properties of the CATE.