BAYESIAN PROPENSITY SCORE ESTIMATORS: INCORPORATING UNCERTAINTIES IN PROPENSITY SCORES INTO CAUSAL INFERENCE
BAYESIAN PROPENSITY SCORE ESTIMATORS: INCORPORATING UNCERTAINTIES IN PROPENSITY SCORES INTO CAUSAL INFERENCE
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DOI：
10.1111/j.1467-9531.2010.01226.x
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发表时间：
2010-01-01
期刊：
影响因子：
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通讯作者：
An, Weihua
中科院分区：
文献类型：
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作者：
An, Weihua
尽管他们的流行，传统的倾向得分估计（PSE）不考虑倾向得分的不确定性。本文开发了贝叶斯倾向得分估计（BPSEs）模型的联合可能性的倾向得分和结果在一个步骤中，自然地将这种不确定性的因果推理。模拟结果表明，PSE使用估计的倾向分数往往高估的变化，在估计的治疗效果，也就是说，他们往往提供大于必要的标准误差，并导致过于保守的推断，而BPSE提供正确的标准误差估计的治疗效果和有效的推断。与其他方差调整方法相比，BPSE保证提供正的标准误差，在小样本中更可靠，可以很容易地用于推断个体治疗效果等。为了说明所提出的方法，BPSE被应用于评估工作培训计划。作者的网站上有附带的软件。
Despite their popularity, conventional propensity score estimators (PSEs) do not take into account uncertainties in propensity scores. This paper develops Bayesian propensity score estimators (BPSEs) to model the joint likelihood of both propensity score and outcome in one step, which naturally incorporates such uncertainties into causal inference. Simulations show that PSEs using estimated propensity scores tend to overestimate variations in the estimates of treatment effects that is, too often they provide larger than necessary standard errors and lead to overly conservative inference whereas BPSEs provide correct standard errors for the estimates of treatment effects and valid inference. Compared with other variance adjustment methods, BPSEs are guaranteed to provide positive standard errors, more reliable in small samples, can be readily employed to draw inference on individual treatment effects, etc. To illustrate the proposed methods, BPSEs are applied to evaluating a job training program. Accompanying software is available on the author's website.