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Propensity Score Modeling and Evaluation

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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

In causal inference for binary treatments, the propensity score is defined as the probability of receiving the treatment given covariates. Under the ignorability assumption, causal treatment effects can be estimated by conditioning on/adjusting for the propensity scores. However, in observational studies, propensity scores are unknown and need to be estimated from the observed data. Estimation of propensity scores is essential in making reliable causal inference. In this chapter, we first briefly discuss the modeling of propensity scores for a binary treatment; then we will focus on the estimation of the generalized propensity scores for categorical treatment variables with more than two levels and continuous treatment variables. We will review both parametric and nonparametric approaches for estimating the generalized propensity scores. In the end, we discuss how to evaluate the performance of different propensity score models and how to choose an optimal one among several candidate models.

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Authors and Affiliations

  1. Department of Statistics & Actuarial Science, University of Waterloo, Waterloo, ON, Canada

    Yeying Zhu & Lin (Laura) Lin

Authors
  1. Yeying Zhu
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  2. Lin (Laura) Lin
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Correspondence to Yeying Zhu .

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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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Zhu, Y., (Laura) Lin, L. (2016). Propensity Score Modeling and Evaluation. 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_6

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