ISyE Seminar Series: Johann D. Gaebler
"A Simple, Statistically Robust Test of Discrimination"
Johann D. Gaebler
Ph.D. candidate in Statistics at Harvard University and a Stone Ph.D. Scholar
About the Seminar:
In observational studies of discrimination, the most common statistical approaches consider either the rate at which decisions are made (benchmark tests) or the success rate of those decisions (outcome tests). Both tests, however, have well-known statistical limitations, sometimes suggesting discrimination even when there is none. Despite the fallibility of the benchmark and outcome tests individually, here we prove a surprisingly strong statistical guarantee: under a common non-parametric assumption, at least one of the two tests must be correct; consequently, when both tests agree, they are guaranteed to yield correct conclusions. We present empirical evidence that the underlying assumption holds approximately in several important domains, including lending, education, and criminal justice—and that our hybrid test is robust to the moderate violations of the assumption that we observe in practice. Applying this approach to 2.8 million police stops across California, we find evidence of widespread racial discrimination.
About the Speaker:
Johann D. Gaebler is a Ph.D. candidate in Statistics at Harvard University and a Stone Ph.D. Scholar studying the measurement of discrimination. He focuses on applying data science and statistical tools to complex social issues such as mass incarceration and hiring discrimination. His research interests span causal inference, computational social science, Bayesian statistics, machine learning, and algorithmic fairness. Before coming to Harvard, Johann was a Ph.D. student in Computational and Mathematical Engineering and a Knight-Hennessy Scholar at Stanford University, where he worked at the Stanford Computational Policy Lab. His interdisciplinary approach aims to bridge statistics, computer science, and policy to address pressing social challenges.
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