Chapter

Statistical Causal Inferences and Their Applications in Public Health Research

Part of the series ICSA Book Series in Statistics pp 111-124

Date:

Propensity Score Modeling and Evaluation

  • Yeying ZhuAffiliated withDepartment of Statistics & Actuarial Science, University of Waterloo Email author 
  • , Lin (Laura) LinAffiliated withDepartment of Statistics & Actuarial Science, University of Waterloo

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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.