Maria Cuellar is an assistant professor of Criminology and Statistics at the University of Pennsylvania. Her research focuses on causal inference and data analysis in the law. She is a researcher at the Center for Statistics and Applications in Forensic Evidence, where she studies how statistics can improve forensic practice.

Researchers often need to determine whether a specific exposure, or something else, caused an individual's outcome. To answer questions of causality in which the exposure and outcome have already been observed, researchers have suggested estimating the probability of causation (PC). PC is especially important in court—for example, in class action lawsuits—and in public and health policy, in determining who has benefited most from a program. However, the current estimation methods for PC make strong parametric assumptions or are inefficient and do not easily yield inferential tools.

In this talk, Dr. Cuellar will describe an influence-function-based nonparametric estimator for a projection of PC, which allows for simple interpretation and valid inference by making only weak structural assumptions. Finally, she will present applications of the proposed estimator using criminology datasets and ask: What does it mean when the treatment effect is positive, but the probability of causation is low?

This event has in-person and virtual attendance options—attend in person in Ritter Annex 992 or connect via Zoom at 2 p.m. on April 11: https://temple.zoom.us/j/95759227419

Suitable points will be given to graduate and undergraduate students for attending.