Empirical Bayes Estimation of Treatment Effects with Many A/B Tests: An Overview

Eduardo Azevedo, Alex Deng, José L. Montiel Olea and E. Glen Weyl

AEA Papers and Proceedings, 2019, vol. 109, 43-47

Abstract: The use of large-scale experimentation to screen product innovations is increasingly common. This is a practical guide on how to use treatment effect estimates from a large number of experiments to improve estimates of the effects of each experiment. When thousands of new features are A/B tested by internet companies, the winners tend to be a combination of good features and features that got lucky experimental draws. Empirical Bayes methods are a commonly used tool in statistics to separate good features from lucky draws. We give a user-friendly overview of both classic and recent approaches to this problem.

JEL-codes: C11 C21 C31 C51 O31 (search for similar items in EconPapers)
Date: 2019
Note: DOI: 10.1257/pandp.20191003
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Citations: View citations in EconPapers (1)

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