Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects.
Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects.
复制标题
倾向得分估计的不确定性：变量选择和模型平均因果效应的贝叶斯方法。
DOI：
10.1080/01621459.2013.869498
复制
发表时间：
2014
影响因子：
3.7
通讯作者：
Dominici,Francesca
中科院分区：
文献类型：
--
作者：
Zigler,CorwinMatthew;Dominici,Francesca
观察性数据的因果推断通常依赖于倾向评分（PS）的概念，以调整观察到的混杂因素的治疗比较。随着“大数据”时代的决策越来越依赖于大量复杂的数字数据集，研究人员经常面临的决策是，哪些高维协变量集包括在PS模型中，以满足估计平均因果效应所需的假设。通常，采用简单或临时方法来获得单个PS模型，而不承认与模型选择相关的不确定性。我们提出了三种贝叶斯方法PS变量选择和模型平均，（a）选择相关变量从一组候选变量，包括在PS模型和（B）估计因果治疗效果的加权平均值估计在不同的PS模型。每个PS模型的相关权重反映了对该模型调整必要变量的能力的数据驱动支持。我们用模拟研究说明了我们提出的方法的特点，并最终使用我们的方法来比较2606名医疗保险受益人中脑肿瘤手术与非手术治疗的有效性。本文的补充材料可在网上查阅。
Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of “big data” are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include in the PS model to satisfy the assumptions necessary for estimating average causal effects. Typically, simple or ad hoc methods are employed to arrive at a single PS model, without acknowledging the uncertainty associated with the model selection. We propose three Bayesian methods for PS variable selection and model averaging that (a) select relevant variables from a set of candidate variables to include in the PS model and (b) estimate causal treatment effects as weighted averages of estimates under different PS models. The associated weight for each PS model reflects the data-driven support for that model’s ability to adjust for the necessary variables. We illustrate features of our proposed approaches with a simulation study, and ultimately use our methods to compare the effectiveness of surgical versus nonsurgical treatment for brain tumors among 2606 Medicare beneficiaries. Supplementary materials for this article are available online.