Observational Data for Heterogeneous Treatment Effects with Application to Recommender Systems

EC'19 Proceedings of the 2019 ACM Conference on Economics and Computation

15 Pages Posted: 27 Jun 2018 Last revised: 19 Sep 2019

See all articles by Akos Lada

Akos Lada

Harvard University

Alexander Peysakhovich

Yale University - Human Cooperation Lab

Diego Aparicio

Massachusetts Institute of Technology (MIT), Department of Economics

Michael Bailey

Facebook

Date Written: May 2019

Abstract

Decision makers in health, public policy, technology, and social science are increasingly interested in going beyond ‘one-size-fits-all’ policies to personalized ones. Thus, they are faced with the problem of estimating heterogeneous causal effects. Unfortunately, estimating heterogeneous effects from randomized data requires large amounts of statistical power and while observational data is often available in much larger quantities the presence of unobserved confounders can make using estimates derived from it highly suspect. We show that under some assumptions estimated heterogeneous treatment effects from observational data can preserve the rank ordering of the true heterogeneous causal effects. Such an approach is useful when observational data is large, the set of features is high-dimensional, and our priors about feature importance are weak. We probe the
effectiveness of our approach in simulations and show a real-world example in a large-scale recommendations problem.

Keywords: Heterogeneous treatment effects, Machine learning, Online platforms, Personalization, Recommender systems, Social networks

JEL Classification: C5, C53, M31, C9

Suggested Citation

Lada, Akos and Peysakhovich, Alexander and Aparicio, Diego and Bailey, Michael, Observational Data for Heterogeneous Treatment Effects with Application to Recommender Systems (May 2019). EC'19 Proceedings of the 2019 ACM Conference on Economics and Computation. Available at SSRN: https://ssrn.com/abstract=3190359 or http://dx.doi.org/10.2139/ssrn.3190359

Akos Lada

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Alexander Peysakhovich

Yale University - Human Cooperation Lab ( email )

New Haven, CT
United States

Diego Aparicio (Contact Author)

Massachusetts Institute of Technology (MIT), Department of Economics ( email )

Cambridge, MA
United States

Michael Bailey

Facebook ( email )

1601 S. California Ave.
Palo Alto, CA 94304
United States

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