Commentary: The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?
Commentary: The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?
复制标题
DOI：
10.1093/ije/dyw227
复制
发表时间：
2016-12-01
影响因子：
7.7
通讯作者：
Vansteelandt S
中科院分区：
文献类型：
--
作者：
Daniel RM;De Stavola BL;Vansteelandt S
最近的两篇文章，一篇由Vandenbroucke，Broadbent和皮尔斯（以下简称VBP）1和另一篇由Krieger和Davey Smith（以下简称KDS）2批评了这两组作者所描述的流行病学中现代“因果推理”学派的主流。这些作者提出的批评是严厉的; VBP给这个领域贴上了“理论上错误”和“实践上错误”的标签，KDS至少在某些情况下认为这个领域不仅“找错了树”，而且“完全错过了森林”。更具体地说，该学派及其概念和方法被描绘成仅适用于非常狭窄的调查范围，排除了现代流行病学中大多数重要的问题和研究设计，例如遗传变异的影响，种族和性别差异的研究以及使用不密切反映随机对照试验（RCT）的研究设计。此外，这些概念和方法被描绘成即使在它们被认为适用的这个狭窄范围内也可能具有很大的误导性。我们相信，越南船民和儿童安全服务社的批评，大部分是基于对他们所批评的方法的一系列误解。因此，在这个回应中，我们的目标首先是描绘一个更准确的画面的正式因果推理方法，然后概述的关键误解VBP的和KDS的批评。KDS特别批评有向无环图（DAG），使用三个例子来这样做。他们的讨论突出了关于DAG在因果推理中的作用的进一步误解，因此我们在本文的第三部分致力于解决这些问题。在我们的讨论中，我们提出了进一步的反对意见，我们必须在这两篇论文中的论点，在得出结论，从采用严格的框架获得的清晰度是一种资产，而不是一个障碍，更可靠地回答一个非常广泛的因果问题，使用数据从许多不同的设计观察研究。
Two recent articles, one by Vandenbroucke, Broadbent and Pearce (henceforth VBP) 1 and the other by Krieger and Davey Smith (henceforth KDS), 2 criticize what these two sets of authors characterize as the mainstream of the modern ‘causal inference’school in epidemiology. The criticisms made by these authors are severe; VBP label the field both ‘wrong in theory’and ‘wrong in practice’, and KDS—at least in some settings—feel that the field not only ‘bark [s] up the wrong tree’but ‘miss [es] the forest entirely’. More specifically, the school of thought, and the concepts and methods within it, are painted as being applicable only to a very narrow range of investigations, to the exclusion of most of the important questions and study designs in modern epidemiology, such as the effects of genetic variants, the study of ethnic and gender disparities and the use of study designs that do not closely mirror randomized controlled trials (RCTs). Furthermore, the concepts and methods are painted as being potentially highly misleading even within this narrow range in which they are deemed applicable. We believe that most of VBP’s and KDS’s criticisms stem from a series of misconceptions about the approach they criticize. In this response, therefore, we aim first to paint a more accurate picture of the formal causal inference approach, and then to outline the key misconceptions underlying VBP’s and KDS’s critiques. KDS in particular criticize directed acyclic graphs (DAGs), using three examples to do so. Their discussion highlights further misconceptions concerning the role of DAGs in causal inference, and so we devote the third section of the paper to addressing these. In our Discussion we present further objections we have to the arguments in the two papers, before concluding that the clarity gained from adopting a rigorous framework is an asset, not an obstacle, to answering more reliably a very wide range of causal questions using data from observational studies of many different designs.
登录
查看更多内容
影响因子：
5
作者：
De Stavola BL;Daniel RM;Ploubidis GB;Micali N
通讯作者：
Micali N
DOI：
10.1111/j.1467-9868.2009.00703.x
发表时间：
2009-01-01
影响因子：
5.8
作者：
Commenges, Daniel;Gegout-Petit, Anne
通讯作者：
Gegout-Petit, Anne
影响因子：
2
作者：
Bowden, Jack;Vansteelandt, Stijn
通讯作者：
Vansteelandt, Stijn
使用动态因果模型估算肥胖和体重变化对死亡率的影响。
影响因子：
3.7
作者：
Cao B
通讯作者：
Cao B
影响因子：
2
作者：
Chen, Jinbo;Kang, Guolian;VanderWeele, Tyler;Zhang, Cuilin;Mukherjee, Bhramar
通讯作者：
Mukherjee, Bhramar