Angela Zhou

I am a research fellow at the Simons program on causality. In summer 2022, I will start as an Assistant Professor at USC Marshall Data Sciences and Operations in Operations.

Previously I was a FODSI postdoc at UC Berkeley, hosted by Bin Yu, and Michael I. Jordan. I obtained my PhD from Cornell University in Operations Research and Information Engineering working with Nathan Kallus at Cornell Tech. My work was previously supported on a NDSEG fellowship.

My research interests are broadly in statistical machine learning for data-driven decision making under uncertainty, and the interplay of statistics and optimization. My dissertation work developed causal inference and machine learning as a language for prescriptive analytics, making robust recommendations for action in view of fundamental practical challenges in observational/operational data. My work emphasizes credibility as a form of reliability, developing robust inferential procedures subject to analyst-tunable violations of assumptions. I am particularly interested in the implications of real-world complex environments that realize societal impacts of machine learning, such as e-commerce, healthcare and policy, for designing inferential methods and informing prescriptive insights.

My email is

CV. Scholar. A 10-min video on my work.

Selected publications (Full List)

Author ordering on papers is alphabetical, following Operations Research convention.
  1. Stateful Offline Contextual Policy Evaluation and Learning Kallus, Nathan, and Zhou, Angela AISTATS 2022 [Abs]
  2. Minimax-Optimal Policy Learning under Unobserved Confounding Kallus, Nathan, and Zhou, Angela Management Science (Forthcoming), supersedes Neurips 2018 version 2020 [Abs] [Code] [Video]
  3. Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning Kallus, Nathan, and Zhou, Angela Neurips 2020 [Abs] [arXiv] [Code] [Video]
  4. Assessing algorithmic fairness with unobserved protected class using data combination Kallus, Nathan, Mao, Xiaojie, and Zhou, Angela Management Science (Forthcoming). A preliminary version appeared at FaCCT 2020 2019 [Abs] [arXiv] [Code] [Video]