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Propensity Score Analysis with Survey Weighted Data

Author

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  • Ridgeway Greg

    (Department of Criminology, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA 19104-6286, USA)

  • Kovalchik Stephanie Ann
  • Griffin Beth Ann

    (RAND Corporation, Santa Monica, CA, USA)

  • Kabeto Mohammed U.

    (Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA)

Abstract

Propensity score analysis (PSA) is a common method for estimating treatment effects, but researchers dealing with data from survey designs are generally not properly accounting for the sampling weights in their analyses. Moreover, recommendations given in the few existing methodological articles on this subject are susceptible to bias. We show in this article through derivation, simulation, and a real data example that using sampling weights in the propensity score estimation stage and the outcome model stage results in an estimator that is robust to a variety of conditions that lead to bias for estimators currently recommended in the statistical literature. We highly recommend researchers use the more robust approach described here. This article provides much needed rigorous statistical guidance for researchers working with survey designs involving sampling weights and using PSAs.

Suggested Citation

  • Ridgeway Greg & Kovalchik Stephanie Ann & Griffin Beth Ann & Kabeto Mohammed U., 2015. "Propensity Score Analysis with Survey Weighted Data," Journal of Causal Inference, De Gruyter, vol. 3(2), pages 237-249, September.
    • Handle: RePEc:bpj:causin:v:3:y:2015:i:2:p:237-249:n:6
    DOI: 10.1515/jci-2014-0039
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    References listed on IDEAS

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    1. Little R.J., 2004. "To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 546-556, January.
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