Lin Wang

Causal Benefit-Aware Recommendation for Personalized Learning-Path Features: A Targeting-Policy Framework with Provable Guarantees and Randomized Evaluation

This work formalizes feature recommendation as a causal targeting-policy problem: rank students by the estimated conditional average treatment effect (CATE) of a feature and recommend to the top of the ranking, proving causal top-CATE targeting maximizes policy value at any budget and weakly dominates predictive (outco...