Abstract: This study proposes novel estimation and inference approaches for heterogeneous local treatment effects using high-dimensional covariates and observational data without a strong ignorability assumption. To achieve this, with a binary instrumental variable, the parameters of interest are identified on an unobservable subgroup of the population (compliers). Lasso estimation under a non-convex objective function is developed for a two-stage generalized linear model, and a debiased estimator is proposed to construct confidence intervals for treatment effects conditioned on covariates. Notably, this approach simultaneously corrects the biases due to high-dimensional estimation at both stages. The finite sample performance is evaluated via simulation studies, and real data analysis is performed on the Oregon Health Insurance Experiment to illustrate the feasibility of the proposed procedure. Recent progress on heterogeneous quantile treatment effect on compliers will also be discussed.

Bio: 邱宇谋，博士毕业于爱荷华州立大学，先后在内布拉斯加林肯大学和爱荷华州立大学任教。于2023年7月加入北京大学数学科学学院、统计科学中心，职位为长聘副教授。他的研究包括：高维数据分析、高维协方差矩阵和精度矩阵的统计推断、因果分析、缺失数据分析。同时，他也致力于统计方法在精准农业、流行病模型、法医学等领域的应用研究。