[Submitted on 23 Oct 2026]
Title:Optimal Correction Sets for Provable Redress of Contestable Causal Discovery
View a PDF of the paper titled Optimal Correction Sets for Provable Redress of Contestable Causal Discovery, by Fabrizio Russo and 4 other authors
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Abstract:Constraint-based causal discovery relies on conditional-independence (CI) tests that may conflict in finite samples, leaving no causal graph that satisfies them all. Causal Assumption-based Argumentation (ABA) makes these tests explicit as defeasible commitments, and offers correspondence guarantees with a subset of them, but its existing repair procedure releases facts heuristically and offers no formal response to a user's contestation of a release. We argue that contestable causal discovery requires an inspectable repair object, a justification for it, and a predictable revision procedure for such contestations. We therefore formulate CI-fact repair as a minimum-cost correction problem and implement it in Answer Set Programming (ASP). Building on Causal ABA, we provide a compact and efficient ASP encoding of causal consistency, with soundness and completeness guarantees, and a two-way projection correspondence between optimal stable models and minimum-cost correction sets. These results establish fidelity to retained facts, subset-minimality and cost optimality of the repair, conflict-based justifications for released facts, and formally guaranteed redress under user contestations. An empirical evaluation supports the formal account: OptABA-PC retains more correct facts, reduces ambiguity in the compatible outputs, and improves graph reconstruction over Causal ABA on common benchmarks.
Comments: 41 pages including appendix
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.33138 [cs.AI]
(or arXiv:2610.33138v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.33138
arXiv-issued DOI via DataCite
Submission history
From: Fabrizio Russo [view email] [v1] Fri, 23 Oct 2026 16:01:53 UTC (75 KB)
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