A Causal Framework for Observational Studies of Discrimination
In studies of discrimination, researchers often seek to estimate a causal effect of race or gender on outcomes.
Research
The GI Bill was one of the worst racial injustices of the 20th century. Congress can fix it.
Benefits of legislation meant to help all veterans were routinely denied to Black veterans.
Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making
In the dominant paradigm for designing equitable machine learning systems, one works to ensure that model predictions satisfy various fairness criteria, such as parity in error rates across race, gend
Goals and Gaps: Educational Careers of Immigrant Children
We study the educational choices of children of immigrants in a tracked school system.
The Foundational Lawlessness of the Law Itself: Racial Criminalization & the Punitive Roots of Punishment in America
Empirical researchers and criminal justice practitioners have generally set aside history in exchange for behavioral models and methodologies that focus primarily on crime itself as the most measurabl
The Current State of Bail Reform in the United States: Results of a Landscape Analysis of Bail Reforms Across All 50 States
This report discusses cash bail reforms that have occurred in the United States and provides key considerations for people interested in implementing bail reforms in their jurisdiction.
Social science research to inform solar geoengineering
As the prospect of average global warming exceeding 1.5°C becomes increasingly likely, interest in supplementing mitigation and adaptation with solar geoengineering (SG) responses will almost certainl
The US Pretrial System: Balancing Individual Rights and Public Interests
In this article, we review a growing empirical literature on the effectiveness and fairness of the US pretrial system and discuss its policy implications.
Environmental Insights: Conversations on policy and practice from the Harvard Environmental Economics Program
Sheila Olmstead, professor of public affairs at the LBJ School of Public Affairs at the University of Texas, Austin, shared her thoughts on U.S.
Breaking Taboos in Fair Machine Learning: An Experimental Study
Many scholars, engineers, and policymakers believe that algorithmic fairness requires disregarding information about certain characteristics of individuals, such as their race or gender.
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