SMU SOE Seminar (March 10, 2023): Binary Choice with Asymmetric Loss in a Data-rich Environment: Theory and an Application to Racial Justice

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ABSTRACT We study the binary choice problem in a data-rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not offer computationally attractive solutions in data-rich environments. The machine learning literature has many algorithms but is focused mostly on loss functions that are independent of covariates. We show that theoretically valid decisions on binary outcomes with general loss functions can be achieved via a very simple loss-based reweighting of the logistic regression or state-of-the-art machine learning techniques. We apply our analysis to racial justice in pretrial detention. Keywords: binary outcomes, asymmetric losses, machine learning, cost-sensitive classification, pretrial detention. Click here to view the CV. Click here to view the paper.
		PRESENTER Andrii Babii University of North Carolina at Chapel Hill
		RESEARCH FIELDS Econometrics
		DATE: 10 March 2023 (Friday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903