Uplift Modeling & Heterogeneous Treatment Effects
Predicting who is *persuadable*, not who converts — CATE, meta-learners, causal forests and Qini
Target the customers an intervention actually changes, not the ones who would have bought anyway: the move from ATE to the conditional effect tau(x), response models versus uplift models, the four quadrants (persuadables, sure things, lost causes, sleeping dogs) worked by hand with dollars, the data a CATE model needs (randomized logs, known propensities, overlap and balance checks), S-, T-, X-, R- and DR-learners with their bias and variance trade-offs, uplift trees and honest causal forests with confidence intervals, double/debiased machine learning with Frisch-Waugh-Lovell orthogonalization and cross-fitting, evaluation with uplift and Qini curves, AUUC, uplift@k, decile calibration and placebo tests (and why AUC fails), turning CATE into a budgeted cost-aware policy with IPS/DR policy value, multi-arm and continuous treatments, and operating the policy with a three-arm validation A/B. Includes a 50,000-user coupon simulation with a known true effect whose numbers were run in NumPy, scikit-learn and EconML.
Practice questions (6)
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Explain the Four Quadrants With Numbers for a Retention Offer
Intermediate · Free -
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Choose a Meta-Learner When Only 2 Percent of Users Were Treated
Advanced · Free -
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Build and Read a Qini Curve From Decile Counts
Intermediate -
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Design the Validation A/B Test for a Targeting Policy
Advanced -
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Explain Orthogonalization and Cross-Fitting in Double ML
Advanced