Angela Zhou
Connect with Angela
About Angela
Zhou works on applications-motivated methodology in statistical machine learning, operations management, and causal inference, including on algorithmic fairness. Her applied work has developed robust disparity measurement for algorithmic fairness in the provision of algorithmically-mediated social services. She has also collaborated with criminal justice practitioners to use causal inference methods to evaluate the impacts of bail reform.
Contributions
In the News
Publications
Evaluates the short-term effects of New York’s Bail Elimination Act, which eliminated money bail and pretrial detention for most misdemeanor and nonviolent felony defendants. Focuses on the impact of this reform on crime rates, specifically for assault, theft, burglary, robbery, and drug crimes. Finds no significant impact on assault, theft, and drug crimes but notes a statistically significant increase in robbery. Suggests further research is needed to understand the long-term impacts of bail reform on crime rates and deterrence.
Discusses challenges in implementing treatment recommendations in consequential domains where adherence is voluntary, focusing on how variations in individual characteristics can predict both the likelihood of treatment uptake and the eventual outcomes. Emphasizes fairness considerations in treatment allocation, addressing gaps in service utilization among those who could benefit most. Proposes a two-stage algorithm for solving policy decisions under general constraints, aiming to mitigate variance and ensure optimal decision-making.