Valid inference of cause-and-effect relations in observational studies necessitates adjusting for common causes of the focal predictor (i.e., treatment) and the outcome. When such common causes, henceforth termed confounders, remain unadjusted for, they generate spurious correlations that lead to biased causal effect estimates. But routine adjustment for all available covariates, when only a subset are truly confounders, is known to yield potentially inefficient and unstable estimators. In this article, we introduce a data-driven confounder selection strategy that focuses on stable estimation of the treatment effect. The approach exploits the causal knowledge that after adjusting for confounders to eliminate all confounding biases, adding any remaining non-confounding covariates associated with only treatment or outcome, but not both, should not systematically change the effect estimator. The strategy proceeds in two steps. First, we prioritize covariates for adjustment by probing how strongly each covariate is associated with treatment and outcome. Next, we gauge the stability of the effect estimator by evaluating its trajectory adjusting for different covariate subsets. The smallest subset that yields a stable effect estimate is then selected. Thus, the strategy offers direct insight into the (in)sensitivity of the effect estimator to the chosen covariates for adjustment. The ability to correctly select confounders and yield valid causal inferences following data-driven covariate selection is evaluated empirically using extensive simulation studies. Furthermore, we compare the introduced method empirically with routine variable selection methods. Finally, we demonstrate the procedure using two publicly available real-world datasets. A step-by-step practical guide with user-friendly R functions is included. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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通过关注效应估计量的稳定性来进行混杂调整的数据驱动协变量选择。

观察性研究中因果关系的有效推断需要调整焦点预测因素（即治疗）和结果的常见原因。当此类常见原因（此后称为混杂因素）仍未调整时，它们会产生虚假相关性，从而导致因果效应估计出现偏差。但是，众所周知，当只有一个子集是真正的混杂因素时，对所有可用协变量进行常规调整会产生可能效率低下且不稳定的估计量。在本文中，我们介绍了一种数据驱动的混杂因素选择策略，该策略侧重于对治疗效果的稳定估计。该方法利用因果知识，即在针对混杂因素进行调整以消除所有混杂偏差后，添加任何剩余的仅与治疗或结果相关的非混杂协变量，而不是两者，不应该系统地改变效果估计器。该策略分两步进行。首先，我们通过探究每个协变量与治疗和结果的关联程度来确定协变量的优先级以进行调整。接下来，我们通过评估其针对不同协变量子集的轨迹调整来衡量效果估计器的稳定性。然后选择产生稳定效果估计的最小子集。因此，该策略提供了对效应估计器对所选协变量进行调整的（不）敏感性的直接洞察。使用广泛的模拟研究根据经验评估在数据驱动的协变量选择之后正确选择混杂因素并产生有效因果推论的能力。此外，我们根据经验将引入的方法与常规变量选择方法进行比较。最后，我们使用两个公开可用的真实世界数据集来演示该过程。包含带有用户友好 R 函数的分步实用指南。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。