Adaptive Experimental Design Using the Propensity Score
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 av...
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| Veröffentlicht in: | Journal of business & economic statistics Jg. 29; H. 1; S. 96 - 108 |
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Taylor & Francis
01.01.2011
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| 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. |
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| AbstractList | 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. [PUBLICATION 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. |
| Author | Hirano, Keisuke Hahn, Jinyong Karlan, Dean |
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| Cites_doi | 10.1287/mnsc.1050.0504 10.1093/biomet/70.1.41 10.1111/1468-0262.00442 10.1257/aer.98.3.1040 10.2307/2284578 10.1016/0304-4076(86)90038-2 10.2307/2998560 10.2307/2342192 10.2307/2983640 10.1214/ss/1177009939 10.1257/aer.97.5.1774 10.1257/aer.91.2.103 |
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| SubjectTerms | Asymptotic methods Conditional probabilities Design efficiency Design of experiments Economic statistics Efficiency bound Estimating techniques Estimators Experiment design Experimental design Fundraising Hunger Probability Propensity score Sample size Software Statistical discrepancies Statistical variance Studies |
| Title | Adaptive Experimental Design Using the Propensity Score |
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