4 papers · 1 filter
Quantifying Individual Risk for Binary Outcomes
Peng Wu, Peng Ding, Zhi Geng +1
Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widel…
Pseudo-strata learning via maximizing misclassification reward
Shanshan Luo, Peng Wu, Zhi Geng
Online advertising aims to increase user engagement and maximize revenue, but users respond heterogeneously to ad exposure. Some users purchase only when exposed to ads, while othe…
Safe Individualized Treatment Rules with Controllable Harm Rates
Peng Wu, Qing Jiang, Shanshan Luo +1
Estimating individualized treatment rules (ITRs) is crucial for tailoring interventions in precision medicine. Typical ITR estimation methods rely on conditional average treatment…
On the Comparative Analysis of Average Treatment Effects Estimation via Data Combination
Peng Wu, Shanshan Luo, Zhi Geng
There is growing interest in exploring causal effects in target populations via data combination. However, most approaches are tailored to specific settings and lack comprehensive…