Adaptive Experimental Design Using the Propensity Score

Jinyong Hahn, Keisuke Hirano and Dean Karlan

Working Papers from Yale University, Department of Economics

Abstract: Many social experiments are run in multiple waves, or are replications of 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.

JEL-codes: C10 C13 C14 C90 C93 (search for similar items in EconPapers)
Date: 2009-01
New Economics Papers: this item is included in nep-cbe, nep-ecm and nep-exp
References: Add references at CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Adaptive Experimental Design Using the Propensity Score (2011)
Journal Article: Adaptive Experimental Design Using the Propensity Score (2011)
Working Paper: Adaptive Experimental Design Using the Propensity Score (2009)
Working Paper: Adaptive Experimental Design Using the Propensity Score (2009)
Working Paper: Adaptive Experimental Design Using the Propensity Score (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:yaleco:59

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