Angela Zhou

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401S Bridge Hall

I am an Assistant Professor at USC Marshall Data Sciences and Operations, in the Operations group.

Previously I was a research fellow at the Simons program on causality and a FODSI postdoc at UC Berkeley. 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 data-driven decision making under uncertainty, including operations, statistical machine learning, and causal inference, and the interplay of statistics and optimization.

Email: zhoua at usc.edu.

[Scholar], [CV],

Interested in working with me? Advising/mentorship plan and FAQ

Here at USC

Research interests by topic:

selected publications

  1. Data-Driven Influence Functions for Causal Inference and Optimization-Based Estimators
    Michael Jordan, Yixin Wang, and Angela Zhou
    Supersedes Empirical Gateaux Derivatives for Causal Inference (oral at Neurips 2022)
  2. Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders
    David Bruns-Smith, and Angela Zhou
    2023
  3. Optimizing and Learning Sequential Assortment Decisions with Platform Disengagement
    Mika Sumida, and Angela Zhou
    2023
  4. Minimax-Optimal Policy Learning under Unobserved Confounding
    Nathan Kallus, and Angela Zhou
    Management Science (2021), supersedes Neurips 2018 version 2023
  5. Assessing algorithmic fairness with unobserved protected class using data combination
    Nathan Kallus, Xiaojie Mao, and Angela Zhou
    Management Science (2020). A preliminary version appeared at FaCCT 2020 2023
  6. Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes
    Ezinne Nwankwo, Lauri Goldkind, and Angela Zhou
    arXiv preprint arXiv:2502.10605 2025
  7. Bridging Prediction and Intervention Problems in Social Systems
    = Liu, Lydia and Raji, Deb and Zhou, Angela and Guerdan, Luke and Hullman, Jessica and Wilder, Bryan and Zhang, Simone and Adam, Hammaad and Coston, Amanda and Laufer, Ben and Malinsky, Daniel and Nwankwo, Ezinne and Zangler-Tishler, Michael and Imai, Kosuke and Wang, Angelina and Kapoor, Sayash and Loftus, Joshua and Sendak, Mark and Wilson, Ashia and Ben-Michael, Eli and Tolbert, Alexander and Nabi, Razieh and Guha, Shion and Salganik, Matt and Narayanan, Arvind
    2025

news

Jun 18, 2025 I received a courtesy joint appointment in the Department of Computer Science! Apply to DSO or CS if you want to work with me, and put my name down.
Jun 17, 2025 We have been working on a whitepaper from the BIRS Bridging Predictions and Interventions in Social Systems Workshop. It’s a great reference on predictive vs. causal targeting in consequential public-sector algorithmic decision making. Working Paper
Feb 17, 2025 We have exciting new work on optimally annotating outcomes for doubly-robust causal inference, motivated by an ongoing collaboration in homelessness services. It works really well! paper
Sep 21, 2023 Delighted to share that my paper on Optimal and Fair Encouragement Policy Evaluation and Learning is accepted at Neurips 2023! TL;DR: under incomplete take-up, we care both about utility and who gets access to services. Let’s work together to equitably reduce administrative burdens. See you in New Orleans!
Sep 21, 2023 Big update to Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders, with new and exciting results on complex healthcare data and warm-starting RL!
Sep 20, 2023 Thanks Microsoft for the Accelerating Foundation Models award!
Aug 30, 2023 Check out our report on EAAMO 2022 in SIGecom Exchanges!
Aug 23, 2023 I’ll be an Area Chair for AISTATS 2023.
Aug 10, 2023 New paper posted on Optimizing and Learning Assortment Decisions in the Presence of Platform Disengagement! (with Mika Sumida)
Jul 1, 2023 Our paper on An Empirical Evaluation of the Impact of New York’s Bail Reform on Crime Using Synthetic Controls was accepted at the journal of Statistics and Public Policy!