Suzuki Etsuji
Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
JMA J. 2025 Apr 28;8(2):323-337. doi: 10.31662/jmaj.2024-0246. Epub 2025 Apr 21.
For several decades, the counterfactual model and the sufficient cause model have shaped our understanding of causation in biomedical science and, more recently, the link between these two models has enabled us to obtain a deeper understanding of causality. In this article, I provide a brief overview of these fundamental causal models using a simple example. The counterfactual model focuses on one particular cause or intervention and gives an account of the various effects of that cause. By contrast, the sufficient cause model considers sets of actions, events, or states of nature which together inevitably bring about the outcome under consideration. In other words, the counterfactual framework addresses the question "what if?" while the sufficient cause framework addresses the question "why does it happen?" Although these two models are distinct and address different causal questions, they are closely related and used to elucidate the same cause-effect relationships. Importantly, the sufficient cause model makes clear that causation is a multifactorial phenomenon, and it is a "finer" model than the counterfactual model; an individual is of one and only one response type in the counterfactual framework, whereas an individual may be at risk of none, one, or several sufficient causes. Understanding the link between the two causal models can provide greater insight into causality and can facilitate the use of each model in appropriate contexts, highlighting their respective strengths. I will briefly present three topics of interest from our research: the relationship between the concepts of confounding and of covariate balance; distinctions between attributable fractions and etiologic fractions; and the identification of operating mediation and mechanism. It is important to scrutinize observed associations in a complementary manner, using both the counterfactual model and the sufficient cause model, employing both inductive and deductive reasoning. This holistic approach will better help us to unravel causality.
几十年来，反事实模型和充分病因模型塑造了我们对生物医学科学中因果关系的理解，最近，这两种模型之间的联系使我们能够更深入地理解因果性。在本文中，我用一个简单的例子简要概述这些基本的因果模型。反事实模型关注一个特定的原因或干预，并描述该原因的各种影响。相比之下，充分病因模型考虑的是一组行动、事件或自然状态，它们共同不可避免地导致所考虑的结果。换句话说，反事实框架解决的问题是“如果……会怎样？”，而充分病因框架解决的问题是“为什么会发生？”。虽然这两种模型截然不同，解决的是不同的因果问题，但它们密切相关，用于阐明相同的因果关系。重要的是，充分病因模型明确表明因果关系是一种多因素现象，并且它是比反事实模型更“精细”的模型；在反事实框架中，个体只有一种且仅有一种反应类型，而个体可能没有、有一种或有几种充分病因的风险。理解这两种因果模型之间的联系可以更深入地洞察因果性，并有助于在适当的背景下使用每种模型，突出它们各自的优势。我将简要介绍我们研究中感兴趣的三个主题：混杂概念与协变量平衡之间的关系；归因分数与病因分数之间的区别；以及操作中介和机制的识别。以互补的方式，使用反事实模型和充分病因模型，运用归纳推理和演绎推理来仔细审查观察到的关联非常重要。这种整体方法将更好地帮助我们揭示因果关系。
