数苑经纬论坛（130）：Learning Optimal Individualized Decision Rules with Conditional Demographic Parity
来源：
发布时间：2026-11-13
报告时间：- 2026年11月21日 （周六）上午9:00-10:00
- 报告地点：
- 6号楼C405
- 报告人：
- 苏雯
- 报告人单位：
- 香港城市大学
- 邀请人：
- 晏挺
- 摘要：
- Individualized decision rules (IDRs) have become increasingly prevalent in societal applications such as personalized marketing, healthcare, and public policy design. However, a critical ethical concern arises from the potential discriminatory effects of IDRs trained on biased data. These algorithms may disproportionately harm individuals from minority subgroups defined by sensitive attributes like gender, race, or language. To address this issue, we propose a novel framework that incorporates demographic parity (DP) and conditional demographic parity (CDP) constraints into the estimation of optimal IDRs. We show that the theoretically optimal IDRs under DP and CDP constraints can be obtained by applying perturbations to the unconstrained optimal IDRs, enabling a computationally efficient solution. Theoretically, we derive convergence rates for both policy value and the fairness constraint term. The effectiveness of our methods is illustrated through comprehensive simulation studies and an empirical application to the Oregon Health Insurance Experiment.
Wen Su（City University Of Hong Kong），Dr. Wen Su received her PhD in Statistics from the University of Hong Kong. She earned a Bachelor of Science in Industrial Engineering from the University of Toronto and a Master of Science in Biostatistics from Columbia University. Her research interests include statistical machine learning, survival analysis, longitudinal data analysis, causal inference, and nonparametric and semiparametric inference. In survival analysis, she develops deep learning–based methods for complex time-to-event data, particularly in medical applications. She also develops methods for optimal individualized treatment regimes to improve the precision and effectiveness of care. She received the NSFC Young Scientist Grant (2024) and the RGC Early Career Scheme Grant (2025). She has published papers in prestigious journals including Journal of the Statistical Royal Society Series B, Biometrika, Biometrics, Bernoulli, and the International Conference on Machine Learning.
