报告题目 Title: Nonparametric Instrumental Variable Regression with Observed Covariates

报告人 Speaker: 陈宗昊

报告人所在单位 Affiliation: University College London

时间 Time: 2025-12-22 15:00-16:00

地点 Venue: Room 2001, Guanghua Eastern Main Tower (Handan Campus)

报告摘要 Abstract: We study the nonparametric instrumental variable regression (NPIV) with observed covariates, termed NPIV-O. Compared to standard NPIV, the additional covariates aid causal identification and allow for heterogeneous causal effect estimation. However, they introduce two challenges for theoretical analysis. First, they induce a partial identity structure, making previous NPIV analyses based on ill-posedness, stability, or link conditions inapplicable. Second, they impose anisotropic smoothness on the structural function. To address the first challenge, we introduce a novel Fourier measure of partial smoothing; for the second, we extend the existing kernel 2SLS instrumental variable algorithm (KIV-O) to incorporate Gaussian kernel lengthscales adaptive to anisotropic smoothness. We prove upper L2-learning rates for KIV-O and the first L2-minimax lower bounds for NPIV-O. Both rates interpolate between known optimal rates for NPIV and nonparametric regression. We also identify a gap between the upper and lower bounds, arising from the choice of kernel lengthscales tuned to minimize a projected risk. Our theoretical analysis also applies to proximal causal inference, an emerging framework for causal effect estimation with the same conditional moment restriction as NPIV-O.

个人简介 Bio: 陈宗昊目前在英国伦敦大学学院基础人工智能中心攻读博士学位，师从 François-Xavier Briol 与 Arthur Gretton。他的研究兴趣聚焦于通过优化与泛化两大视角理解机器学习算法。目前的研究方向包括生成模型、蒙特卡洛方法以及因果推断。在攻读博士之前，他于清华大学电子工程系获得学士学位（2022），并曾获清华大学特等奖学金。

海报 Poster: 陈宗昊 学术报告.jpg