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Adaptive Experimental Design Using the Propensity Score

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  • Jinyong Hahn
  • Keisuke Hirano
  • Dean Karlan

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.

Suggested Citation

  • Jinyong Hahn & Keisuke Hirano & Dean Karlan, 2011. "Adaptive Experimental Design Using the Propensity Score," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 96-108, January.
  • Handle: RePEc:taf:jnlbes:v:29:y:2011:i:1:p:96-108
    DOI: 10.1198/jbes.2009.08161
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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