CHAI’s Joseph Halpern and his student Meir Friedenberg from Cornell University published this paper in the 2019 Conference on Knowledge Representation and Reasoning. The abstract reads:

In many real-world settings, a decision-maker must combine information provided by different experts in order to decide on an effective policy. Alrajeh, Chockler, and Halpern (2018) showed how to combine causal models that are compatible in the sense that, for variables that appear in both models, the experts agree on the causal structure. In this work we show how causal models can be combined in cases where the experts might disagree on the causal structure for variables that appear in both models due to having different focus areas. We provide a new formal definition of compatibility of models in this setting and show how compatible models can be combined. We also consider the complexity of determining whether models are compatible. We believe that the notions defined in this work are of direct relevance to many practical decision making scenarios that come up in natural, social, and medical science settings.