Journal Of The Econometric Society

An International Society for the Advancement of Economic
Theory in its Relation to Statistics and Mathematics

Edited by: Guido W. Imbens • Print ISSN: 0012-9682 • Online ISSN: 1468-0262

Econometrica: Jan, 2021, Volume 89, Issue 1

Adaptive treatment assignment in experiments for policy choice
p. 113-132

Maximilian Kasy, Anja Sautmann

Standard experimental designs are geared toward point estimation and hypothesis testing, while bandit algorithms are geared toward in‐sample outcomes. Here, we instead consider treatment assignment in an experiment with several waves for choosing the best among a set of possible policies (treatments) at the end of the experiment. We propose a computationally tractable assignment algorithm that we call “exploration sampling,” where assignment probabilities in each wave are an increasing concave function of the posterior probabilities that each treatment is optimal. We prove an asymptotic optimality result for this algorithm and demonstrate improvements in welfare in calibrated simulations over both non‐adaptive designs and bandit algorithms. An application to selecting between six different recruitment strategies for an agricultural extension service in India demonstrates practical feasibility.

Log In To View Full Content

Supplemental Material

Supplement to "Adaptive treatment assignment in experiments for policy choice"

This zip file contains the replication files to the manuscript.

Supplement to "Adaptive treatment assignment in experiments for policy choice"

This online appendix contains material not found within the manuscript.

Journal News