Cory McCartan
Christopher T. Kenny
Description: Cory McCartan is a Data Science Assistant Professor / Faculty Fellow at the Center for Data Science at New York University. Hew work on methodological and applied problems in social sciences, with particular emphasis on Bayesian and computational methods and spatial or geographic data. Much of his research has developed or applied algorithmic tools to the problem of legislative redistricting; he helped start the Algorithm-Assisted Redistricting Methodology (ALARM) Project at Harvard University in 2021. He
Estimating Racial Disparities When Race is Not Observed . With Jacob Goldin, Daniel E. Ho, and Kosuke Imai. Software and replication code ; Poster .
The estimation of racial disparities in health care, financial services, voting, and other contexts is often hampered by the lack of individual-level racial information in administrative records. In many cases, the law prohibits the collection of such information to prevent direct racial discrimination. As a result, many analysts have adopted Bayesian Improved Surname Geocoding (BISG), which combines individual names and addresses with the Census data to predict race. Although BISG tends to produce well-cal
Individual and Differential Harm in Redistricting . With Christopher T. Kenny. Replication code
Cory McCartan
Christopher T. Kenny