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Propensity score methods for causal inference: an overview

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Abstract

Propensity score methods are popular and effective statistical techniques for reducing selection bias in observational data to increase the validity of causal inference based on observational studies in behavioral and social science research. Some methodologists and statisticians have raised concerns about the rationale and applicability of propensity score methods. In this review, we addressed these concerns by reviewing the development history and the assumptions of propensity score methods, followed by the fundamental techniques of and available software packages for propensity score methods. We especially discussed the issues in and debates about the use of propensity score methods. This review provides beneficial information about propensity score methods from the historical point of view and helps researchers to select appropriate propensity score methods for their observational studies.

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Authors and Affiliations

  1. Duke University, DUMC 3322, 307 Trent Dr, Durham, NC, 27710, USA

    Wei Pan

  2. University of Central Florida, PO Box 161250, Orlando, FL, 32816, USA

    Haiyan Bai

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  1. Wei Pan
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  2. Haiyan Bai
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Correspondence to Wei Pan.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Communicated by Takahiro Hoshino.

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Pan, W., Bai, H. Propensity score methods for causal inference: an overview. Behaviormetrika 45, 317–334 (2018). https://doi.org/10.1007/s41237-018-0058-8

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  • DOI: https://doi.org/10.1007/s41237-018-0058-8

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