Time: Wednesday, April 1, 2026, 9:30-10:30 a.m.

Venue: Guoshun Campus, Starr Building, Room 403

Moderator: Professor Deyuan Li

Speaker: Associate Professor Wang Miao, Peking University

Title: Identifying the Desert Decision Rule to Assess and Achieve Fairness

Abstract: The fairness of statistical and machine learning models has become a prominent concern. When data encode historical discrimination against certain demographic groups, such as race or gender, models trained on such data may inherit and reproduce these biases, leading to unfair or unwarranted predictions. In this paper, we propose a novel framework for characterising and addressing fairness issues by introducing the notion of desert decision, a latent variable representing the decision an individual rightfully deserves based on their actions, efforts, or abilities. We advocate making decisions by predicting the desert decision, in contrast to existing methods that typically focus on predicting the observed decision subject to fairness constraints. We propose to assess the degree of unfairness in the data by measuring the discrepancy between desert and observed decisions. We establish identification results under causally interpretable assumptions on the fairness property of the desert decision and the unfairness mechanism of the observed decision. For estimation, we develop a sieve maximum likelihood estimator for the target decision rule and an influence-function-based estimator for the degree of unfairness. Sensitivity analysis procedures are further proposed to assess the robustness of our methods to violations of identifying assumptions.

Bio: Wang Miao is currently Associate Professor in the Department of Probability and Statistics and the Center for Statistical Science at Peking University. From 2008 to 2017, he pursued his undergraduate and doctoral studies at the School of Mathematical Sciences, Peking University. From 2017 to 2018, he conducted postdoctoral research in the Department of Biostatistics at Harvard University, and joined Peking University in 2018. His research interests include causal inference, missing data, semiparametric statistics, and their applications. Together with his collaborators, he has proposed a proxy inference theory for confounding analysis, developed identification and doubly robust estimation theory for nonrandom missing data, and advanced semiparametric theory for data fusion. He has received support from the Original Exploratory Program of the National Natural Science Foundation of China and the Young Scientists Project under the National Key Research and Development Program of China. He also serves as Executive Vice President of the Causal Inference Branch of the China Society of Field Statistics.