79 citations · 312 across the 15 of their papers we have counts for
15 papers
Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders
David Bruns-Smith, Angela Zhou
Offline reinforcement learning is important in domains such as medicine, economics, and e-commerce where online experimentation is costly, dangerous or unethical, and where the tru…
A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret
Connor Lawless, Angela Zhou
In this short technical note we propose a baseline for decision-aware learning for contextual linear optimization, which solves stochastic linear optimization when cost coefficient…
Data-Driven Influence Functions for Optimization-Based Causal Inference
Michael I. Jordan, Yixin Wang, Angela Zhou
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inferen…
Off-Policy Evaluation with Policy-Dependent Optimization Response
Wenshuo Guo, Michael I. Jordan, Angela Zhou
The intersection of causal inference and machine learning for decision-making is rapidly expanding, but the default decision criterion remains an \textit{average} of individual cau…
Stateful Offline Contextual Policy Evaluation and Learning
Nathan Kallus, Angela Zhou
We study off-policy evaluation and learning from sequential data in a structured class of Markov decision processes that arise from repeated interactions with an exogenous sequence…
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
Michelle Bao, Angela Zhou, Samantha Zottola +5
Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are commonly used in algorithmic fairness papers due to benchmarking practices of comparing alg…
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
Nathan Kallus, Angela Zhou
Off-policy evaluation of sequential decision policies from observational data is necessary in applications of batch reinforcement learning such as education and healthcare. In such…
Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
Nathan Kallus, Angela Zhou
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual…
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
Nathan Kallus, Xiaojie Mao, Angela Zhou
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algor…
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric
Nathan Kallus, Angela Zhou
Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has cha…
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
Nathan Kallus, Xiaojie Mao, Angela Zhou
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to ind…
Residual Unfairness in Fair Machine Learning from Prejudiced Data
Nathan Kallus, Angela Zhou
Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historica…
Confounding-Robust Policy Improvement
Nathan Kallus, Angela Zhou
We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding. Previous approaches, which assume unco…
Policy Evaluation and Optimization with Continuous Treatments
Nathan Kallus, Angela Zhou
We study the problem of policy evaluation and learning from batched contextual bandit data when treatments are continuous, going beyond previous work on discrete treatments. Previo…
Dynamic Task Allocation for Crowdsourcing Settings
Angela Zhou, Irineo Cabreros, Karan Singh
We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an opt…