#meta-learner

6 notes

• 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.
• Uplift Modeling Uplift is the causal increment that a treatment (campaign exposure, coupon, recommendation) induces in an individual's outcome (purchase, conversion). For binary treatment $W\in\{0,1\}$, outcome $Y$, and covariates $X$.
• 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.