#uplift

3 notes

• 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 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 $X$.