Adaptive experimental design using the propensity score

Jinyong Hahn, Keisuke Hirano, Dean Karlan

Research output: Contribution to journalArticle

11 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 1 2011

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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