The Bayesian Causal Effect Estimation Algorithm
The Bayesian Causal Effect Estimation Algorithm
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DOI：
10.1515/jci-2014-0035
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发表时间：
2015-09-01
影响因子：
1.4
通讯作者：
Atherton, Juli
中科院分区：
文献类型：
--
作者：
Talbot, Denis;Lefebvre, Genevieve;Atherton, Juli
在观察性研究中估计因果暴露效应，理想情况下要求分析人员对应用领域有广泛的知识。研究者经常通过使用完全调整的结果回归模型来绕过与混杂因素的识别和选择相关的困难。然而，由于这些模型可能包含比所需更多的协变量，因此暴露回归系数的方差可能会不必要地大。可以尝试模型选择，而不是使用完全调整的模型。大多数经典的统计模型选择方法，如贝叶斯模型平均，不容易解决因果效应估计。我们提出了一种新的模型平均的因果推理方法，贝叶斯因果效应估计（BCEE），这是由因果推理的图形框架的动机。BCEE旨在无偏地估计连续暴露对连续结果的因果影响，同时比完全调整的方法更有效。
Estimating causal exposure effects in observational studies ideally requires the analyst to have a vast knowledge of the domain of application. Investigators often bypass difficulties related to the identification and selection of confounders through the use of fully adjusted outcome regression models. However, since such models likely contain more covariates than required, the variance of the regression coefficient for exposure may be unnecessarily large. Instead of using a fully adjusted model, model selection can be attempted. Most classical statistical model selection approaches, such as Bayesian model averaging, do not readily address causal effect estimation. We present a new model averaged approach to causal inference, Bayesian causal effect estimation (BCEE), which is motivated by the graphical framework for causal inference. BCEE aims to unbiasedly estimate the causal effect of a continuous exposure on a continuous outcome while being more efficient than a fully adjusted approach.