#cate

5 notes

• Applied Causal Inference for Pricing — CATE & SCM Across Public Datasets An applied case study using only public datasets (LendingClub, iPinYou) that combines CATE estimation for price-sensitivity heterogeneity with SCM-based moderator analysis to design individual-level, risk-based pricing and RTB bidding policies — all findings illustrative and projected, not proprietary.
• CATE (Conditional Average Treatment Effect) The Conditional Average Treatment Effect (CATE) is the average treatment effect given covariates $X=x$:
• DR-Learner The DR-Learner is a two-stage doubly robust estimator for CATE that regresses a pseudo-outcome on the covariates.
• 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.