Long-term Causal Inference Under Persistent Confounding via Data Combination
報告人簡介
王禹皓，清華大學(xué)交叉信息學(xué)院助理教授。本科畢業(yè)于清華大學(xué)自動化系，隨后進入麻省理工學(xué)院計算機和電子工程系攻讀博士學(xué)位，并任職于LIDS實驗室。王禹皓教授在入職清華大學(xué)之前任職于劍橋大學(xué)統(tǒng)計學(xué)實驗室并擔任博士后研究員。王禹皓教授目前的研究興趣集中在：Causal inference (因果推斷)；Experimental design (實驗設(shè)計)；High-dimensional statistics (高維統(tǒng)計)；Distribution-free test (免分布假設(shè)檢驗)等領(lǐng)域。王禹皓教授曾有多篇文章發(fā)表于The Annals of Statistics，JRSSB，Biometrika，Bernoulli等頂尖統(tǒng)計學(xué)期刊以及NeurIPS等頂尖機器學(xué)習與人工智能會議。王禹皓教授還曾入選福布斯中國2021年度30 Under 30榜單：科學(xué)和醫(yī)療健康榜單。
內(nèi)容簡介
We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data. However, both types of data include observations of some short-term outcomes. In this paper, we uniquely tackle the challenge of persistent unmeasured confounders, i.e., some unmeasured confounders that can simultaneously affect the treatment, short-term outcomes and the long-term outcome, noting that they invalidate identification strategies in previous literature. To address this challenge, we exploit the sequential structure of multiple short-term outcomes, and develop three novel identification strategies for the average long-term treatment effect. We further propose three corresponding estimators and prove their asymptotic consistency and asymptotic normality. We finally apply our methods to estimate the effect of a job training program on long-term employment using semi-synthetic data. We numerically show that our proposals outperform existing methods that fail to handle persistent confounders.
