In causal inference, and specifically in the causes‐of‐effects problem, one is interested in how to use statistical evidence to understand causation in an individual case, and in particular how to assess the so‐called probability of causation. The answer involves the use of potential responses, which describe what would have happened to the outcome if we had observed a different value for the exposure. However, even given the best possible statistical evidence for the association between exposure and outcome, we can typically only provide bounds for the probability of causation. Dawid and his colleagues highlighted some fundamental conditions, namely exogeneity, comparability and sufficiency, that are required to obtain such bounds from experimental data. The aim of the present paper is to provide methods to find, in specific cases, the best subsample of the reference data set to satisfy these requirements. For this, we introduce a new variable, expressing the preference whether or not to be exposed, and we set the question up as a model selection problem. The best model is selected by using the marginal probability of the responses and a suitable prior over the model space. An application in the educational field is presented.

中文翻译：

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通过贝叶斯模型选择过程产生影响的原因

在因果推论，特别在原因-的效果问题，一个是兴趣如何使用统计证据来理解因果关系在个别情况下，特别是如何评估所谓的因果关系的概率。答案涉及潜在反应的使用，这些潜在反应描述了如果我们观察到不同的暴露值会对结果产生什么影响。但是，即使给出关于暴露与结果之间联系的最佳可能的统计证据，我们通常也只能提供因果关系的界限。戴维德和他的同事们强调了一些基本条件，即外生性，可比性和充分性，这是从实验数据中获得这种界限所必需的。本文的目的是提供在特定情况下找到满足这些要求的最佳参考数据集子样本的方法。为此，我们引入了一个新变量，表示是否要公开的偏好，并将该问题设置为模型选择问题。通过使用响应的边际概率和模型空间上的适当先验来选择最佳模型。介绍了在教育领域的应用。