Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments

46 Pages Posted: 4 Jan 2023

See all articles by John A. List

John A. List

University of Chicago - Department of Economics

Ian Muir

Lyft, Inc.

Gregory Sun

University of Chicago

Multiple version iconThere are 2 versions of this paper

Date Written: December 19, 2023

Abstract

This study investigates how to use regression adjustment to reduce variance in experimental data. We show that the estimators recommended in the literature satisfy an orthogonality property with respect to the parameters of the adjustment. This observation greatly simplifies the derivation of the asymptotic variance of these estimators and allows us to solve for the efficient regression adjustment in a large class of adjustments. Our efficiency results generalize a number of previous results known in the literature. We then discuss how this efficient regression adjustment can be feasibly implemented. We show the practical relevance of our theory in two ways. First, we use our efficiency results to improve common practices currently employed in field experiments. Second, we show how our theory allows researchers to robustly incorporate machine learning techniques into their experimental estimators to minimize variance.

JEL Classification: C9,C90,C91,C93

Suggested Citation

List, John A. and Muir, Ian and Sun, Gregory, Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments (December 19, 2023). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2022-167, Available at SSRN: https://ssrn.com/abstract=4317936 or http://dx.doi.org/10.2139/ssrn.4317936

John A. List (Contact Author)

University of Chicago - Department of Economics ( email )

1126 East 59th Street
Chicago, IL 60637
United States

Ian Muir

Lyft, Inc. ( email )

San Francisco, CA

Gregory Sun

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
30
Abstract Views
183
PlumX Metrics