Causal inference without counterfactuals
Causal inference without counterfactuals
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DOI：
10.2307/2669377
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发表时间：
2000-06-01
影响因子：
3.7
通讯作者：
Dawid, AP
中科院分区：
文献类型：
--
作者：
Dawid, AP
构建和回答因果问题的一种流行方法依赖于反事实的概念：如果世界发展不同，就会观察到的结果；例如，如果病人接受了不同的治疗。根据定义，人们永远无法观察到这样的数量，也无法从经验上评估关于它们的任何建模假设的有效性，即使一个人的结论可能对这些假设很敏感。在这里，我认为，为了对应用原因的可能结果进行推断，反事实论证是不必要的，而且可能具有误导性。提出了一种基于贝叶斯决策分析的替代方法。反事实的性质与推断观察到的结果的可能原因有关，但必须密切关注查询的性质和背景，以及哪些结论可以或不可以得到经验支持。特别是，即使在没有统计不确定性的情况下，这种推论也可能受到不可减少的模糊性程度的影响。
A popular approach to the framing and answering of causal questions relies on the idea of counterfactuals: outcomes that would have been observed had the world developed differently; for example, if the patient had received a different treatment. By definition one can never observe such quantities, nor assess empirically the validity of any modeling assumptions made about them, even though one's conclusions may be sensitive to these assumptions. Here I argue that for making inference about the likely effects of applied causes, counterfactual arguments are unnecessary and potentially misleading. An alternative approach, based on Bayesian decision analysis, is presented. Properties of counterfactuals are relevant to inference about the likely causes of observed effects, but close attention then must be given to the nature and context of the query, as well as to what conclusions can and cannot be supported empirically. In particular, even in the absence of statistical uncertainty, such inferences may be subject to an irreducible degree of ambiguity.