A Mixture Model and Task Allocation Scheme for Crowdsourcing. - Irineo Cabreros; Karan Singh; Angela Zhou (CoRR, 2017)

Policy Evaluation and Optimization with Continuous Treatments. - Nathan Kallus; Angela Zhou (AISTATS, 2018)

Residual Unfairness in Fair Machine Learning from Prejudiced Data. - Nathan Kallus; Angela Zhou (ICML, 2018)

Confounding-Robust Policy Improvement. - Nathan Kallus; Angela Zhou (NeurIPS, 2018)

Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. - Nathan Kallus; Xiaojie Mao; Angela Zhou (AISTATS, 2019)

Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. - Nathan Kallus; Angela Zhou (NeurIPS, 2019)

The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric. - Nathan Kallus; Angela Zhou (NeurIPS, 2019)

Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds. - Nathan Kallus; Angela Zhou (CoRR, 2019)

Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. - Nathan Kallus; Angela Zhou (NeurIPS, 2020)

Fairness, Welfare, and Equity in Personalized Pricing. - Nathan Kallus; Angela Zhou (FAccT, 2021)

It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. - Michelle Bao; Angela Zhou; Samantha Zottola; Brian Brubach; Sarah Desmarais; Aaron Horowitz; Kristian Lum; Suresh Venkatasubramanian (NeurIPS Datasets and Benchmarks, 2021)

An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls. - Angela Zhou; Andrew Koo; Nathan Kallus; Rene Ropac; Richard Peterson; Stephen Koppel; Tiffany Bergin (CoRR, 2021)

Minimax-Optimal Policy Learning Under Unobserved Confounding. - Nathan Kallus; Angela Zhou (Manag. Sci., 2021)

Stateful Offline Contextual Policy Evaluation and Learning. - Nathan Kallus; Angela Zhou (AISTATS, 2022)

Off-Policy Evaluation with Policy-Dependent Optimization Response. - Wenshuo Guo; Michael I. Jordan; Angela Zhou (NeurIPS, 2022)

Empirical Gateaux Derivatives for Causal Inference. - Michael I. Jordan; Yixin Wang 002; Angela Zhou (NeurIPS, 2022)

A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret. - Connor Lawless; Angela Zhou (CoRR, 2022)

Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. - Nathan Kallus; Xiaojie Mao; Angela Zhou (Manag. Sci., 2022)

Optimal and Fair Encouragement Policy Evaluation and Learning. - Angela Zhou (NeurIPS, 2023)

Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders. - David Bruns-Smith; Angela Zhou (CoRR, 2023)

Report on the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2022). - Elena Falcettoni; Dina Machuve; Bryan Wilder; Angela Zhou (SIGecom Exch., 2023)

Reward-Relevance-Filtered Linear Offline Reinforcement Learning. - Angela Zhou (AISTATS, 2024)

Reduced-Rank Multi-objective Policy Learning and Optimization. - Ezinne Nwankwo; Michael I. Jordan; Angela Zhou (CoRR, 2024)

Multi-CATE: Multi-Accurate Conditional Average Treatment Effect Estimation Robust to Unknown Covariate Shifts. - Christoph Kern 001; Michael P. Kim; Angela Zhou (CoRR, 2024)

Orthogonalized Estimation of Difference of Q-functions. - Angela Zhou (CoRR, 2024)

Multi-Accurate CATE is Robust to Unknown Covariate Shifts. - Christoph Kern 001; Michael P. Kim; Angela Zhou (Trans. Mach. Learn. Res., 2024)

Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes. - Ezinne Nwankwo; Lauri Goldkind; Angela Zhou (CoRR, 2025)

Bridging Prediction and Intervention Problems in Social Systems. - Lydia T. Liu; Inioluwa Deborah Raji; Angela Zhou; Luke Guerdan; Jessica Hullman; Daniel Malinsky; Bryan Wilder; Simone Zhang; Hammaad Adam; Amanda Coston; Benjamin Laufer; Ezinne Nwankwo; et al. (CoRR, 2025)