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Effects of Causes and Causes of Effects
Annual Review of Statistics and Its Application ( IF 7.4 ) Pub Date : 2022-03-07 , DOI: 10.1146/annurev-statistics-070121-061120
A. Philip Dawid 1 , Monica Musio 2
Affiliation  

We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention (effects of causes) and studying whether there is a causal link between the observed exposure and outcome in an individual case (causes of effects). For each of these, we introduce and compare various formal frameworks that have been proposed for that purpose, including the decision-theoretic approach, structural equations, structural and stochastic causal models, and potential outcomes. We argue that counterfactual concepts are unnecessary for studying effects of causes but are needed for analyzing causes of effects. They are, however, subject to a degree of arbitrariness, which can be reduced, though not in general eliminated, by taking account of additional structure in the problem.

中文翻译:


原因的影响和影响的原因

我们描述并对比了统计因果关系的两个不同问题领域:研究干预的可能影响(原因的影响)和研究观察到的暴露与个案结果之间是否存在因果关系(影响的原因)。对于其中的每一个,我们介绍并比较了为此目的提出的各种形式框架,包括决策理论方法、结构方程、结构和随机因果模型以及潜在结果。我们认为,反事实概念对于研究原因的影响是不必要的,但对于分析影响的原因却是必需的。然而,它们受到一定程度的任意性的影响,可以通过考虑问题中的额外结构来减少,尽管通常不会消除。

更新日期:2022-03-07
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