"Simple Rules for Decision-Making"
ABSTRACT: Doctors, judges, and other experts typically rely on experience and intuition rather than statistical models when making decisions, often at the cost of significantly worse outcomes. I'll present a simple and intuitive strategy for creating statistically informed decision rules that are easy to apply, easy to understand, and perform on par with state-of-the art machine learning methods in many settings. I'll illustrate these rules with two applications to the criminal justice system: investigatory stop decisions and pretrial detention decisions.
BIO: Ravi Shroff is an Assistant Professor of Applied Statistics in the Department of Applied Statistics, Social Science, and Humanities at NYU’s Steinhardt School, and by joint appointment, an Assistant Professor of Urban Informatics at CUSP.
Dr. Shroff’s interests are broadly related to computational social science, in particular, the application of statistical and machine learning techniques to a variety of urban issues. His current research examines the use of interpretable statistical models to inform pretrial detention decisions; methods to accurately measure gunfire-related crime and reporting rates; and predictive models to support child welfare practitioners.
