Association and Causation: Attributes and Effects of Judges in Equal Employment Opportunity Commission Litigation Outcomes
Michael Sobel
Professor, Columbia University
Date: Friday, May 6, 2022
Time: 11am–noon
Virtual seminar live-stream: The talk will be live-streamed via Zoom. For connection information, please contact statistics@gmu.edu.
Slides are available here.
Abstract
A large literature on judicial decision making asks if judges with different features of an attribute (e.g, sex, race) adjudicate cases differently. Researchers estimate models for case outcomes, interpreting coefficients associated with attributes as effects. But attributes are not treatments. While these coefficients indicate how judges with different features adjudicate the different cases they are assigned, ideally, different judges should be compared on a common set of cases. We construct a general methodology for making such comparisons, using it to study whether monetary relief in discrimination cases prosecuted by the Equal Employment Opportunity Commission differs by judge’s race. For all federal judges (treatments) eligible to hear a case (unit), we define potential outcomes, using unit treatment effects between judges with different features to define a unit feature comparison (UFC), then using these to define new population estimands: the average (AFC) and quantile (QFC) feature comparisons. We estimate these quantities by combining observed case outcomes with missing potential outcomes imputed from the posterior predictive distribution of a two part Bayesian hierarchical model. A case assigned to a minority race judge is more likely to result in monetary relief than were that case assigned to an eligible majority race judge. For the amount of relief, the 95% posterior interval for the AFC covers 0, while the upper endpoint of the 95% posterior interval for the median QFC is negative.
About the speaker
Michael Sobel is a professor of Statistics at Columbia University. His research interests lie primarily in the area of causal inference, where he has published papers on the subjects of mediation, interference, longitudinal causal inference using fixed effects models, meta-analysis, compliance, and causal inference for fMRI experiments, in which massive amounts of time series data are collected for subjects under varying experimental conditions. In addition to extending his work on fMRI, he is also working on interference in observational studies using fixed effects models, and he is working to develop some new estimands for counterfactual inference more broadly.