AIMay 20, 2020

Combining the Causal Judgments of Experts with Possibly Different Focus Areas

arXiv:2005.10131v15 citations
Originality Synthesis-oriented
AI Analysis

This work addresses practical decision-making scenarios in natural, social, and medical sciences where experts have varying focus areas, but it is incremental as it builds on prior work by Alrajeh, Chockler, and Halpern [2018].

The paper tackles the problem of combining causal models from experts who may disagree on causal structure due to different focus areas, by providing a new formal definition of compatibility and showing how to combine such models, with analysis of the complexity of determining compatibility.

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.

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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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