KR2024Proceedings of the 21st International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning

Hanoi, Vietnam. November 2-8, 2024.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-05-8

Sponsored by
Published by

Copyright © 2024 International Joint Conferences on Artificial Intelligence Organization

A Representation Theorem for Causal Decision Making

  1. Joseph Y. Halpern(Cornell University)
  2. Evan Piermont(Royal Holloway, University of London)

Keywords

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

Abstract

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.