A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study

被引:0
|
作者
David Kaplan
Jianshen Chen
机构
[1] University of Wisconsin–Madison,Department of Educational Psychology
来源
Psychometrika | 2012年 / 77卷
关键词
propensity score analysis; Bayesian inference;
D O I
暂无
中图分类号
学科分类号
摘要
A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for three methods of implementation: propensity score stratification, weighting, and optimal full matching. Three simulation studies and one case study are presented to elaborate the proposed two-step Bayesian propensity score approach. Results of the simulation studies reveal that greater precision in the propensity score equation yields better recovery of the frequentist-based treatment effect. A slight advantage is shown for the Bayesian approach in small samples. Results also reveal that greater precision around the wrong treatment effect can lead to seriously distorted results. However, greater precision around the correct treatment effect parameter yields quite good results, with slight improvement seen with greater precision in the propensity score equation. A comparison of coverage rates for the conventional frequentist approach and proposed Bayesian approach is also provided. The case study reveals that credible intervals are wider than frequentist confidence intervals when priors are non-informative.
引用
收藏
页码:581 / 609
页数:28
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