MAO Xiaojie

MAO Xiaojie is an Associate Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management. Their research focuses on causal inference and data-driven decision making. MAO holds a PhD in Statistics and Data Science from Cornell University and a B.A. in Mathematical Economics from Wuhan University. They teach courses such as Empirical Methods in Management Science, Data Analytics: Inference and Decision Making, and Probability Theory and Mathematical Statistics. MAO's work includes publications in top journals and conferences, including Operations Research, the Journal of the Royal Statistical Society Series B, and the Neural Information Processing Systems conference. Their research explores topics such as online joint assortment-inventory optimization, contextual linear optimization, and causal inference under confounding.

Xiaojie Mao is an Associate Professor at the School of Economics and Management, Tsinghua University, specializing in data-driven decision making, causal inference, and statistical machine learning. His research focuses on algorithmic fairness, unobserved confounding, and efficient estimation of treatment effects. He explores methods to assess disparity when protected classes are unobserved and develops techniques for causal inference under challenging data conditions. His work includes interval estimation of individual-level causal effects, stochastic optimization forests, and doubly robust off-policy evaluation. Mao's research bridges theoretical advancements with practical applications in statistical learning and decision-making frameworks.