Hanoi, Vietnam. November 2-8, 2024.

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ISSN: 2334-1033

ISBN: 978-1-956792-05-8

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Copyright © 2024 International Joint Conferences on Artificial Intelligence Organization

- Modeling and reasoning about preferences-General
- Philosophical foundations of KR-General
- Reasoning about actions and change, action languages-General

We show that it is possible to understand and identify a decision maker’s subjective causal judgements by observing her preferences over interventions. Following Pearl [2000, DOI: doi.org/10.1017/S0266466603004109 ], we represent causality using causal models (also called structural equations models), where the world is described by a collection of variables, related by equations. We show that if a preference relation over interventions satisfies certain axioms (related to standard axioms regarding counterfactuals), then we can define (i) a causal model, (ii) a probability capturing the decision-maker’s uncertainty regarding the external factors in the world and (iii) a utility on outcomes such that each intervention is associated with an expected utility and such that intervention A is preferred to B iff the expected utility of A is greater than that of B. In addition, we characterize when the causal model is unique. Thus, our results allow a modeler to test the hypothesis that a decision maker’s preferences are consistent with some causal model and to identify causal judgements from observed behavior.