Effect heterogeneity and variable selection for standardizing causal effects to a target population
Effect heterogeneity and variable selection for standardizing causal effects to a target population
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DOI：
10.1007/s10654-019-00571-w
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发表时间：
2019-10-26
影响因子：
13.6
通讯作者：
Suzuki, Etsuji
中科院分区：
文献类型：
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作者：
Huitfeldt, Anders;Swanson, Sonja A.;Suzuki, Etsuji
随机试验和其他用于因果推断的研究的参与者通常不能代表临床决策者所看到的人群。为了解释人群之间的差异，研究人员可能会考虑将结果标准化为目标人群。我们讨论了与标准化相关的几种不同类型的同质性条件：效果度量的同质性，反事实结果状态转换参数的同质性，以及反事实分布的同质性。这些条件中的每一个都可以用来表明，给定有关科学背景的假设，特定的标准化程序将导致对目标人群影响的无偏估计。我们比较和对比的同质性条件，特别是他们的影响，选择协变量的标准化和他们的影响，如何计算标准化的因果效应在目标人群。虽然最近开发的一些反事实的方法来概括依赖于同质性条件，避免了许多与传统方法相关的问题，他们往往需要调整一个大的（可能是不可行的）协变量集。
The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in an unbiased estimate of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates.