#cate

노트 5개

• 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) Conditional Average Treatment Effect (CATE)는 covariate $X=x$가 주어졌을 때의 평균 처치 효과:
• DR-Learner DR-Learner는 CATE 추정을 위한 2단계 doubly robust estimator로, Pseudo-outcome을 covariate에 대해 regression하는 방식.
• Meta-learners Meta-learners는 기존 supervised learning 방법 (base learner)을 활용하여 CATE를 추정하는 알고리즘의 총칭.
• R-Learner R-Learner (Residualized Learner)는 Robinson Transformation을 기반으로 residualized outcome과 residualized treatment를 사용하여 CATE를 추정하는 Meta-learners.