Minimum intervention cover of a causal graph

Kandasamy, S and Bhattacharyya, A and Honavar, VG (2019) Minimum intervention cover of a causal graph. In: Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 27 January - 1 February 2019, Hilton Hawaii Village Honolulu; United States, pp. 2876-2885.

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Abstract

Eliciting causal effects from interventions and observations is one of the central concerns of science, and increasingly, artificial intelligence. We provide an algorithm that, given a causal graph G, determines MIC(G), a minimum intervention cover of G, i.e., a minimum set of interventions that suffices for identifying every causal effect that is identifiable in a causal model characterized by G. We establish the completeness of do-calculus for computing MIC(G). MIC(G) effectively offers an efficient compilation of all of the information obtainable from all possible interventions in a causal model characterized by G. Minimum intervention cover finds applications in a variety of contexts including counterfactual inference, and generalizing causal effects across experimental settings. We analyze the computational complexity of minimum intervention cover and identify some special cases of practical interest in which MIC(G) can be computed in time that is polynomial in the size of G. © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org).

Item Type:	Conference Paper
Publication:	33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
Publisher:	AAAI Press
Additional Information:	The copyright of this article belongs to AAAI Press
Keywords:	Calculations; Graph algorithms; Microwave integrated circuits, Causal graph; Causal model; Do-calculus; Minimum interventions, Artificial intelligence
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	03 Mar 2021 05:59
Last Modified:	30 Aug 2022 07:09
URI:	https://eprints.iisc.ac.in/id/eprint/66716

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