Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence
Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence
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DOI：
10.1016/j.ssmph.2019.100526
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发表时间：
2020-04-01
影响因子：
4.7
通讯作者：
Glymour, M. Maria
中科院分区：
文献类型：
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作者：
Matthay, Ellicott C.;Hagan, Erin;Glymour, M. Maria
来自不同领域的人口健康研究人员经常解决类似的实质性问题，但依赖于不同的研究设计，反映了他们的本学科。在涉及因果推理的研究中尤其如此，语义和实质性差异阻碍了跨学科对话和合作。在本文中，我们将非随机研究设计分为两类：使用混杂控制的研究设计（例如回归调整或倾向得分匹配）和依赖工具的研究设计（例如工具变量、回归不连续性或双重差异方法）。使用 Shadish、Cook 和 Campbell 框架来评估有效性威胁，我们对比了这两种方法的假设、优点和局限性，并通过教育和健康文献中的例子说明了差异。在各个学科中，所有检验假设的因果关系的方法都涉及无法验证的假设，并且很少有明确的理由完全依赖一种方法。每种方法都需要在统计功效、内部有效性、测量质量和普遍性之间进行权衡。混杂控制方法和基于仪器的方法之间的选择应以这些权衡为指导，并考虑该领域先前工作的最重要的局限性。我们的目标是促进对人口健康研究中可用于因果推断的方法以及它们之间的权衡的共识；鼓励研究人员客观地评估可以从本学科之外的方法中学到什么；并促进选择最能回答研究者的科学问题的方法。
Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions.