Angela Zhou

I am a postdoc at UC Berkeley. In spring 2022, I will be 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.

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 ambiguity), 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.

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 2021 [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]


Oct 16, 2021 New paper, Stateful Offline Contextual Policy Evaluation and Learning
Oct 15, 2021 Talks at INFORMS 2021, Session SD33 (in person). And Berkeley 10/29: Semi-Autonomous Systems Seminar; BLISS Seminar
Aug 1, 2021 Moving to Berkeley for postdoc. Ping if you’re around!
Jun 24, 2021 Giving a talk at Center for Causal Inference Symposium