Econometrics: Constrained classification and policy learning
Published: 4 February 2022
11 February. Professor Alex Tetenov, University of Geneva
Professor Alex Tetenov, University of Geneva
'Constrained classification and policy learning' (co-authored by T. Kitagawa & S. Sakaguchi)
February 11, 4.00pm-5.30pm
Zoom online seminar
Abstract
Modern machine learning approaches to classification, including AdaBoost, support vector machines, and deep neural networks, utilise surrogate loss techniques to circumvent the computational complexity of minimising empirical classification risk. These techniques are also useful for causal policy learning problems, since estimation of individualised treatment rules can be cast as a weighted (cost-sensitive) classification problem. Consistency of the surrogate loss approaches studied in Zhang (2004) and Bartlett et al. (2006) crucially relies on the assumption of correct specification, meaning that the specified set of classifiers is rich enough to contain a first-best classifier. This assumption is, however, less credible when the set of classifiers is constrained by interpretability or fairness, leaving the applicability of surrogate loss based algorithms unknown in such second-best scenarios. This paper studies consistency of surrogate loss procedures under a constrained set of classifiers without assuming correct specification. We show that in the setting where the constraint restricts the classifier's prediction set only, hinge losses (i.e., ℓ1-support vector machines) are the only surrogate losses that preserve consistency in second-best scenarios. If the constraint additionally restricts the functional form of the classifier, consistency of a surrogate loss approach is not guaranteed even with hinge loss. We therefore characterise conditions for the constrained set of classifiers that can guarantee consistency of hinge risk minimising classifiers. Exploiting our theoretical results, we develop robust and computationally attractive hinge loss based procedures for a monotone classification problem.
Biography
Aleksey Tetenov is Professor of Economics at the Geneva School of Economics and Management, University of Geneva. He obtained his Ph.D. in Economics at Northwestern University in 2008. Following his doctoral studies, he was Assistant Professor at Collegio Carlo Alberto (Turin, Italy) and at the University of Bristol. Professor Tetenov’s research combines economic theory, econometrics, and statistics. It is focused on improving how we conduct randomised experiments, and how we effectively use their results in policy decisions.
Further information: business-events@glasgow.ac.uk
First published: 4 February 2022
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