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Applications of Small Area Estimation to Generalization With Subclassification by Propensity Scores

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  • Wendy Chan

    (University of Pennsylvania)

Abstract

Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score–based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods such as subclassification by propensity score, where limited sample sizes lead to sparse strata. This article explores the potential of small area estimation methods to improve the precision of estimators in sparse strata using population data as a source of auxiliary information to borrow strength. Results from simulation studies identify the conditions under which small area estimators outperform conventional estimators and the limitations of this application to causal generalization studies.

Suggested Citation

  • Wendy Chan, 2018. "Applications of Small Area Estimation to Generalization With Subclassification by Propensity Scores," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 182-224, April.
    • Handle: RePEc:sae:jedbes:v:43:y:2018:i:2:p:182-224
    DOI: 10.3102/1076998617733828
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    References listed on IDEAS

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    1. Robert B. Olsen & Larry L. Orr & Stephen H. Bell & Elizabeth A. Stuart, 2013. "External Validity in Policy Evaluations That Choose Sites Purposively," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 32(1), pages 107-121, January.
    2. Brian K Lee & Justin Lessler & Elizabeth A Stuart, 2011. "Weight Trimming and Propensity Score Weighting," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-6, March.
    3. Elizabeth A. Stuart & Stephen R. Cole & Catherine P. Bradshaw & Philip J. Leaf, 2011. "The use of propensity scores to assess the generalizability of results from randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 369-386, April.
    4. Colm O'Muircheartaigh & Larry V. Hedges, 2014. "Generalizing from unrepresentative experiments: a stratified propensity score approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 195-210, February.
    5. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
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