arXiv cs.AI· Fabrizio Russo, Giuseppe Mazzotta, Carmine Dodaro, Francesco Ricca, Francesca Toni·· 12 小时前AI 评分22
面向可争议因果发现可证明救济的最优修正集
Optimal Correction Sets for Provable Redress of Contestable Causal Discovery
AI 导读
研究将因果发现中的 CI 事实修正形式化为最小代价修正问题,并用 Answer Set Programming 实现 OptABA-PC,在最优稳定模型与最小代价修正集之间建立双向投影对应。该编码具备可靠性与完备性保证,可给出被释放事实的冲突依据,并在用户提出争议时提供形式化保证的救济。在常见基准上,OptABA-PC 比 Causal ABA 保留更多正确事实、减少兼容输出歧义并改善图重建。
正文
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