講座題目：Partial identification for unmeasured confounding with applications in causal inference and algorithmic fairness
內容摘要：Unmeasured confounding is one major challenge to reliable causal inference and decision making from observational data. The existence of unmeasured confounders can render the parameter of interest unidentifiable, i.e., it is impossible to learn the parameter even with infinite amount of observed data. As a result, the usual point estimators are generally biased, which may lead to misleading causal conclusions, invalidate evaluation of decision rules, and generate harmful personalized decisions. In this talk, I will present partial identification analysis, a general approach to deal with unmeasured confounding. Partial identification analysis aims to learn all possible values of the parameters of interest under reasonable confounding. I will demonstrate this technique in two applications: (1) estimating the conditional average treatment effect that is important for deriving personalized decision rule; (2) evaluating the outcome disparity of decisions (e.g., loan application approval, recidivism risk assessment algorithm, etc.) with respect to some protected attributes (e.g., race and ethnicity) when the protected attributes cannot be observed directly and must be estimated on an auxiliary dataset.
主講人簡介 ：Xiaojie Mao (毛小介) is a PhD candidate in Department of Statistics and Data Science at Cornell University, and is currently based in Cornell Tech campus in New York City. He researches at the intersection of causal inference and statistical machine learning. He is particularly interested in developing flexible and robust statistical methods for data-driven decision-making that involves causal reasoning. Prior to his graduate study, he was an undergraduate student studying mathematical economics at Wuhan University. See https://xiaojiemao.github.io/ for more information.