报告人： 夏俊文 博士研究生

报告题目： Nash Equilibrium Individualized Treatment Regimes under Network Interference

时间： 2026年10月30日（周五）15:00—15:40

地点： 线上腾讯会议（会议号：826-857-372）

报告摘要：

Network interference creates a fundamental tension in optimal individualized treatment regime learning: maximizing overall population outcomes may come at the expense of some individuals' outcomes. Existing optimal individualized treatment regime methods under interference primarily target population-level objectives without accounting for individual optimality.

Motivated by this tension, we introduce Nash equilibrium individualized treatment regimes, or simply Nash regimes, which directly target individual optimality under interference. Under a Nash regime, no individual can improve their conditional expected outcome through unilateral deviation when all other individuals follow the regime. We establish conditions for the existence of Nash regimes. When no exact Nash regime exists, we introduce an (\alpha^*)-Nash regime as an alternative target that minimizes regret relative to individual optimality.

We then develop Nash learning, a doubly robust procedure for estimating the proposed regimes. Under a super-population random-graph framework accommodating both sparse and dense networks, we characterize how network sparsity and treatment assignment mechanisms affect estimation performance. Simulations and a real-data application demonstrate that Nash learning improves individual optimality while maintaining competitive population-level performance.

报告人简介：

夏俊文，中国人民大学统计学院博士研究生，现于新加坡国立大学联合培养。主要研究方向为个体化最优治疗决策、因果推断与统计机器学习，重点关注复杂数据环境下最优治疗策略的识别、估计及其理论性质。

研究内容涵盖分位数最优治疗策略、工具变量方法、生存数据分析及网络干扰下的个体化治疗决策等，致力于研究治疗效应异质性、未测量混杂、数据删失及个体间干扰等复杂统计问题。相关研究成果已发表于 Journal of the American Statistical Association（JASA）、Statistics and Computing 等国际学术期刊。