Adaptive Experimental Design: Prospects and Applications in Political Science

90 Pages Posted: 5 Jun 2019 Last revised: 9 Nov 2020

See all articles by Molly Offer-Westort

Molly Offer-Westort

Stanford University, Graduate School of Business

Alexander Coppock

Independent

Donald P. Green

Columbia University

Date Written: February 12, 2019

Abstract

Experimental researchers in political science frequently face the problem of inferring which of several treatment arms is most effective. They may also seek to estimate mean outcomes under that arm, construct confidence intervals, and test hypotheses. Ordinarily, multi-arm trials conducted using static designs assign participants to each arm with fixed probabilities. However, a growing statistical literature suggests that adaptive experimental designs that dynamically allocate larger assignment probabilities to more promising treatments are better equipped to discover the best-performing arm. Using simulations and empirical applications, we explore the conditions under which such designs hasten the discovery of superior treatments and improve the precision with which their effects are estimated. Recognizing that many scholars seek to assess performance relative to a control condition, we also develop and implement a novel adaptive algorithm that seeks to maximize the precision with which the largest treatment effect is estimated.

Suggested Citation

Offer-Westort, Molly and Coppock, Alexander and Green, Donald P., Adaptive Experimental Design: Prospects and Applications in Political Science (February 12, 2019). Available at SSRN: https://ssrn.com/abstract=3364402 or http://dx.doi.org/10.2139/ssrn.3364402

Molly Offer-Westort (Contact Author)

Stanford University, Graduate School of Business ( email )

Stanford, CA
United States

Alexander Coppock

Independent

Donald P. Green

Columbia University ( email )

7th Floor, International Affairs Bldg.
420 W. 118th Street
New York, NY 10027
United States

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