Adaptive experimental design using the propensity score

Jinyong Hahn*, Keisuke Hirano, Dean Karlan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Many social experiments are run in multiple waves or replicate earlier social experiments. In principle, the sampling design can be modified in later stages or replications to allow for more efficient estimation of causal effects. We consider the design of a two-stage experiment for estimating an average treatment effect when covariate information is available for experimental subjects. We use data from the first stage to choose a conditional treatment assignment rule for units in the second stage of the experiment. This amounts to choosing the propensity score, the conditional probability of treatment given covariates. We propose to select the propensity score to minimize the asymptotic variance bound for estimating the average treatment effect. Our procedure can be implemented simply using standard statistical software and has attractive large-sample properties.

Original languageEnglish (US)
Pages (from-to)96-108
Number of pages13
JournalJournal of Business and Economic Statistics
Volume29
Issue number1
DOIs
StatePublished - Jan 2011

Funding

The authors thank the editor, the associate editor, two anonymous referees, and numerous seminar and conference participants for their comments and suggestions. Kyle Hood and Mario Samano provided research assistance. Hahn was supported in part by the National Science Foundation under grant SES-0819638. Hirano was supported in part by a grant from the Eller College of Management at the University of Arizona. Karlan was supported in part by the National Science Foundation under grant SES-0547898. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors and do not necessarily reflect those of the National Science Foundation or the Eller College of Management.

Keywords

  • Efficiency bound
  • Experimental design
  • Propensity score

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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