arXiv cs.AI· Fabrizio Russo, Giuseppe Mazzotta, Carmine Dodaro, Francesco Ricca, Francesca Toni·· 2 小时前AI 评分31
基于最优修正集的可争议因果发现可证明补救研究(arXiv 论文)
Optimal Correction Sets for Provable Redress of Contestable Causal Discovery
AI 导读
研究者将约束式因果发现中的 CI 事实修复形式化为最小代价修正问题,并用 Answer Set Programming(ASP)实现,提出 OptABA-PC。
正文 · 原文
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)
来源:arXiv cs.AI · arxiv.org