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Toward a clearer understanding of causal concepts in epidemiology

机译：更加清楚地了解流行病学中的因果概念

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Purpose: In this manuscript, I share insights into causal concepts that emerged from creating and refining a simple example originally designed for teaching causal epidemiologic concepts. Methods: The insights that emerged are primarily related to the difference between how a causal effect occurs in an individual and what our methods assume about how a causal effect occurs when we estimate its effect in a population. In an individual, the causal effect of exposure on disease occurrence results from the interaction of several causal factors in that individual, not from a single factor in isolation. The result of this interaction within an individual determines an individual's causal type (e.g., doomed, exposure causative, exposure preventive, immune) with respect to a particular exposure contrast and target (etiologic) time period. In a population, the causal effect of exposure on disease frequency depends on the distribution of causal types of individuals in that population, not necessarily on the population distribution of covariates. Yet in epidemiology, when we attempt to estimate the effect of a potential cause of interest, we (through the methods we use) usually do not account for this within-individual causal interaction. Results: This failure to account for within-individual causal interactions has interesting implications for causal inference, as I illustrate here: (1) an effect estimate can be simultaneously confounded and unconfounded, (2) there can be confounding even if no variables satisfy the traditional criteria for being considered a confounder, (3) there can be no confounding even if there are variables that do satisfy the traditional confounder criteria, (4) the magnitude of confounding caused by a variable need not depend on the strength of the exposure-variable association, (5) a directed acyclic graph does not always correctly identify the presence of confounding, (6) the common-cause confounder criterion is imperfect, and (7) a time-varying confounder does not necessarily lead to time-varying confounding. Conclusions: Our example illustrates that confounding is a "team sport": single variables do not confound by themselves; confounding depends on how variables interact in individuals, not just on how variables are distributed within and across populations. Because confounding depends on how variables interact in individuals, methods that ignore causal interactions in individuals are not guaranteed to be confounding-identification methods.
机译：目的：在本手稿中，我分享对因果概念的见解，这些因果概念是通过创建和完善最初旨在教授因果流行病学概念的简单示例而产生的。方法：产生的见解主要与因果效应在个体中的发生方式以及我们估计种群中因果效应的方法在我们的方法中假设因果效应如何发生之间的差异有关。在个体中，暴露对疾病发生的因果关系是由于该个体中几种因果因素的相互作用所致，而不是由孤立的单个因素所致。个体内这种相互作用的结果决定了个体相对于特定暴露对比和目标（病因）时间段的因果类型（例如，注定的，暴露致病性，预防暴露，免疫）。在人群中，暴露对疾病发生频率的因果关系取决于该人群中个体因果类型的分布，而不必取决于协变量的人群分布。然而，在流行病学中，当我们试图估计潜在的潜在原因的影响时，我们（通过我们使用的方法）通常不会考虑这种个体内部因果关系。结果：这种无法解释个体内部因果关系的相互作用对因果推论产生了有趣的影响，正如我在此处说明的那样：（1）效果估计可以同时混淆和不混淆，（2）即使没有变量满足条件估计也可能造成混淆被视为混杂因素的传统标准，（3）即使存在确实满足传统混杂因素标准的变量，也不会存在混杂问题，（4）变量引起的混杂程度不必取决于暴露程度-变量关联，（5）有向无环图并不总是能正确识别混杂的存在，（6）常见原因的混杂因素标准不完善，并且（7）时变混杂因素不一定会导致时变混杂。结论：我们的示例说明混杂是一种“团队运动”：单个变量自身不会混淆;相反，变量本身不会混淆。混淆不仅取决于变量在个人中的相互作用方式，还不仅取决于变量在人群内部和人群之间的分布方式。由于混淆取决于变量在个体中的交互方式，因此不能保证忽略个体中因果关系的方法是混淆识别方法。

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