报告题目：Double machine learning of continuous treatment effects with general instrumental variables

报告人：崔逸凡 长聘副教授 浙江大学

报告时间：2026年7月21日9:00-10:00

报告地点：伍卓群楼第二报告厅

校内联系人：朱复康 [email protected]

报告摘要：

  Estimating causal effects of continuous treatments is a common problem in practice, for example, in studying average dose-response functions. Classical analyses typically assume that all confounders are fully observed, whereas in real-world applications, unmeasured confounding often persists. In this article, we propose a novel framework for the identification of average dose-response functions using instrumental variables, thereby mitigating bias induced by unobserved confounders. We introduce the concept of a uniform regular weighting function and consider covering the treatment space with a finite collection of open sets. On each of these sets, such a weighting function exists, allowing us to identify the average dose-response function locally within the corresponding region. For estimation, we propose an augmented inverse probability weighted score for continuous treatments with instrumental variables under a debiased machine learning framework, and provide practical guidance to adaptively establish regular weighting functions from the data. We further establish the asymptotic properties when the average dose-response function is estimated via kernel regression or empirical risk minimization. Finally, we conduct both simulation and empirical studies to assess the finite-sample performance of the proposed methods.

报告人简介：

  崔逸凡，浙江大学长聘副教授（研究员），博士生导师。北卡罗来纳大学教堂山分校统计与运筹专业博士（2018），曾任宾夕法尼亚大学沃顿商学院博士后研究员、新加坡国立大学统计与数据科学系助理教授。国家级青年人才计划入选者（2021）。