1.6k citations · 2.9k across the 36 of their papers we have counts for
6 papers · 1 filter
Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
Zeyang Jia, Eli Ben-Michael, Kosuke Imai
Algorithmic decisions and recommendations are used in many high-stakes decision-making settings such as criminal justice, medicine, and public policy. We investigate whether it wou…
Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods
Christopher T. Kenny, Cory McCartan, Shiro Kuriwaki +2
The United States Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct the first independent…
Priming bias versus post-treatment bias in experimental designs
Matthew Blackwell, Jacob R. Brown, Sophie Hill +2
Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential mode…
Making Differential Privacy Work for Census Data Users
Cory McCartan, Tyler Simko, Kosuke Imai
The U.S. Census Bureau collects and publishes detailed demographic data about Americans which are heavily used by researchers and policymakers. The Bureau has recently adopted the…
A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate
Adeline Lo, Santiago Olivella, Kosuke Imai
Many networks in political and social research are bipartite, with edges connecting exclusively across two distinct types of nodes. A common example includes cosponsorship networks…
Estimating Racial Disparities When Race is Not Observed
Cory McCartan, Robin Fisher, Jacob Goldin +2
The estimation of racial disparities in various fields is often hampered by the lack of individual-level racial information. In many cases, the law prohibits the collection of such…