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Causal entropy optimization
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Branchini, N., Aglietti, Virginia, Dhir, N. and Damoulas, Theodoros (2023) Causal entropy optimization. In: 26th International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, 25 - 27 Apr 2023. Published in: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 206 pp. 8586-8605. ISSN 2640-3498.
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Official URL: https://proceedings.mlr.press/v206/branchini23a.ht...
Abstract
We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed. This problem arises in many areas of science including biology, operations research and healthcare. We propose Causal Entropy Optimization (CEO), a framework that generalizes Causal Bayesian Optimization (CBO) to account for all sources of uncertainty, including the one arising from the causal graph structure. CEO incorporates the causal structure uncertainty both in the surrogate models for the causal effects and in the mechanism used to select interventions via an information-theoretic acquisition function. The resulting algorithm automatically trades-off structure learning and causal effect optimization, while naturally accounting for observation noise. For various synthetic and real-world structural causal models, CEO achieves faster convergence to the global optimum compared with CBO while also learning the graph. Furthermore, our joint approach to structure learning and causal optimization improves upon sequential, structure-learning-first approaches.
Item Type: | Conference Item (Paper) | ||||||||
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Subjects: | Q Science > QA Mathematics | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Random variables, Mathematical optimization, Algorithms | ||||||||
Journal or Publication Title: | Proceedings of The 26th International Conference on Artificial Intelligence and Statistics | ||||||||
Publisher: | ML Research Press | ||||||||
ISSN: | 2640-3498 | ||||||||
Official Date: | 2023 | ||||||||
Dates: |
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Volume: | 206 | ||||||||
Page Range: | pp. 8586-8605 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Copyright Holders: | Copyright © The authors and PMLR 2023. MLResearchPress | ||||||||
Date of first compliant deposit: | 20 February 2023 | ||||||||
Date of first compliant Open Access: | 23 August 2023 | ||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||
Title of Event: | 26th International Conference on Artificial Intelligence and Statistics (AISTATS) | ||||||||
Type of Event: | Conference | ||||||||
Location of Event: | Valencia, Spain | ||||||||
Date(s) of Event: | 25 - 27 Apr 2023 | ||||||||
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