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Overview of Propensity Score Methods

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Statistical Causal Inferences and Their Applications in Public Health Research

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

The propensity score methods are widely used to adjust confounding effects in observational studies when comparing treatment effects. The propensity score is defined as the probability of treatment assignment conditioning on some observed baseline characteristics and it provides a balanced score for the treatment conditions as conditioning on the propensity score, the treatment groups are comparable in terms of the baseline covariates. In this chapter, we will first provide an overview of the propensity score and the underlying assumptions for using propensity score, we will then discuss four methods based on propensity score: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score, as well as the differences among the four methods.

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Author information

Authors and Affiliations

  1. Department of Epidemiology, School of Public Health & Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA

    Hua He & Jiang He

  2. College of Basic Science and Information Engineering, Yunnan Agricultural University, Yunnan, 650201, China

    Jun Hu

Authors
  1. Hua He
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  2. Jun Hu
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  3. Jiang He
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Correspondence to Hua He .

Editor information

Editors and Affiliations

  1. Department of Epidemiology School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA

    Hua He

  2. Christiana Care Health System, Value Institute, Newark, Delaware, USA

    Pan Wu

  3. School of Social Work and Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

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He, H., Hu, J., He, J. (2016). Overview of Propensity Score Methods. In: He, H., Wu, P., Chen, DG. (eds) Statistical Causal Inferences and Their Applications in Public Health Research. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-41259-7_2

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