#uplift

노트 3개

• Customer Segmentation & Causal Targeting — An Applied Case Study An end-to-end applied case study on the public Dunnhumby dataset — NMF latent factors and K-Means segmentation feeding meta-learner / Causal Forest HTE and an OPE-validated optimal targeting policy, with a candid look at positivity violation and counter-intuitive "sleeping dog" segments.
• Dunnhumby — Track 2: Causal Targeting via Heterogeneous Treatment Effects Meta-learner / Causal Forest CATE under severe positivity violation (PS AUC 0.989); an OPE-validated policy targets ~31% of customers and surfaces counter-intuitive negative-CATE segments. Hypothesis-generating on public data.
• Uplift Modeling Uplift(증분효과)는 처치(캠페인 노출·쿠폰·추천)가 한 개인의 결과(구매·전환)에 미치는 인과적 증분이다. 이진 처치 $W\in\{0,1\}$, 결과 $Y$, covariate $X$에 대해