aRAND Corporation, Pittsburgh, PA; bEducational Testing Service, Princeton, NJ; and cInstitute for Social Research, University of Michigan, Ann Arbor, MI.
Epidemiology. 2017 Nov;28(6):802-811. doi: 10.1097/EDE.0000000000000734.
Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.
使用观察数据估计暴露（与某些对照相比）对结果的因果效应，通常需要解决暴露组和对照组在可能与结果相关的暴露前特征（混杂因素）上存在差异的事实。倾向评分方法长期以来一直被用作调整观察到的混杂因素的工具，以便在强可忽略性假设下产生更有效的因果效应估计。在本文中，我们比较了两种有前途的倾向评分估计方法（用于时间不变的二分类暴露），用于评估治疗组的平均治疗效果：广义增强模型和协变量平衡倾向评分，主要目的是为分析师在这两种方法之间选择提供一些经验法则。我们比较了这些方法在不同维度上的表现，包括外生变量的存在、暴露或结果与协变量之间关系的复杂性以及结果和暴露的剩余方差。我们发现，当结果或暴露与协变量之间存在非复杂关系时，协变量平衡方法优于增强方法，但在复杂关系下，增强方法表现更好。我们提出了一种方法应该优于另一种方法的标准，而不是一概而论地说一种方法总是优于另一种方法。
