It is often of interest to use observational data to estimate the causal effect of a target exposure or treatment on an outcome. When estimating the treatment effect, it is essential to appropriately adjust for selection bias due to observed confounders using, for example, propensity score weighting. Selection bias due to confounders occurs when individuals who are treated are substantially different from those who are untreated with respect to covariates that are also associated with the outcome. A comparison of the unadjusted, naive treatment effect estimate with the propensity score adjusted treatment effect estimate provides an estimate of the selection bias due to these observed confounders. In this article, we propose methods to identify the observed covariate that explains the largest proportion of the estimated selection bias. Identification of the most influential observed covariate or covariates is important in resource‐sensitive settings where the number of covariates obtained from individuals needs to be minimized due to cost and/or patient burden and in settings where this covariate can provide actionable information to healthcare agencies, providers, and stakeholders. We propose straightforward parametric and nonparametric procedures to examine the role of observed covariates and quantify the proportion of the observed selection bias explained by each covariate. We demonstrate good finite sample performance of our proposed estimates using a simulation study and use our procedures to identify the most influential covariates that explain the observed selection bias in estimating the causal effect of alcohol use on progression of Huntington's disease, a rare neurological disease.

中文翻译：

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量化由于在因果治疗效果估计中观察到的个体混杂因素造成的偏差。

使用观察数据来估计目标暴露或治疗对结果的因果影响通常很有趣。在估计治疗效果时，必须适当调整由于观察到的混杂因素导致的选择偏差，例如，使用倾向得分加权。当接受治疗的个体与未接受治疗的个体在与结果相关的协变量方面存在显着差异时，就会出现由于混杂因素导致的选择偏差。将未经调整的、朴素的治疗效果估计值与倾向评分调整后的治疗效果估计值进行比较，可以对由于这些观察到的混杂因素造成的选择偏差进行估计。在本文中，我们提出了识别可解释最大比例估计选择偏差的观察协变量的方法。在资源敏感的环境中，由于成本和/或患者负担而需要最小化从个体获得的协变量的数量，以及在该协变量可以为医疗保健机构提供可操作信息的环境中，确定最有影响的观察到的一个或多个协变量非常重要，提供者和利益相关者。我们提出了简单的参数和非参数程序来检查观察到的协变量的作用并量化每个协变量解释的观察到的选择偏差的比例。我们使用模拟研究证明了我们提出的估计的良好有限样本性能，并使用我们的程序来确定最有影响力的协变量，这些协变量解释了在估计酒精使用对亨廷顿病进展的因果影响时观察到的选择偏差，