Combining Experts' Causal Judgments
This addresses decision-making under uncertainty for policymakers, but it appears incremental as it builds on existing causal modeling frameworks.
The paper tackles the problem of a policymaker needing to combine multiple experts' causal judgments to decide on the most effective intervention, by formally defining effective interventions and providing methods to merge compatible causal models or decompose incompatible ones.
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.