论文标题
结合专家的因果判断
Combining Experts' Causal Judgments
论文作者
论文摘要
考虑一个想要决定要执行哪种干预措施以改变当前不良情况的决策者。该决策者可以掌握一个专家团队，每个团队都对不同因素之间的因果关系有理解，从而导致结果。决策者对专家的意见具有不同程度的信心。她想结合他们的意见，以决定最有效的干预措施。我们正式定义有效干预的概念，然后考虑如何将专家的因果判断组合在一起以确定最有效的干预措施。我们定义了两个因果模型为\ emph {兼容}的概念，并显示如何合并兼容的因果模型。然后，我们将其作为结合专家因果关系判断的基础。我们还为因果模型提供了分解的定义，以适应模型不兼容的情况。我们在许多现实生活中说明了我们的方法。
Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts' opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts' causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being \emph{compatible}, and show how compatible causal models can be merged. We then use it as the basis for combining experts' causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.
