Optimal Correction Sets for Provable Redress of Contestable Causal Discovery

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Computer Science > Artificial Intelligence arXiv:2610.33138 (cs) [Submitted on 23 Oct 2026] Title: Optimal Correction Sets for Provable Redress of Contestable Causal Discovery Authors: Fabrizio Russo, Giuseppe Mazzotta, Carmine Dodaro, Francesco Ricca, Francesca Toni.

计算机科学 > 人工智能 arXiv:2610.33138 (cs) [提交于 2026 年 10 月 23 日] 标题：用于可争议因果发现的可证明补救的最优修正集作者：Fabrizio Russo, Giuseppe Mazzotta, Carmine Dodaro, Francesco Ricca, Francesca Toni。

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.

摘要：基于约束的因果发现依赖于条件独立性（CI）测试，这些测试在有限样本中可能会发生冲突，导致没有因果图能满足所有测试。基于因果假设的论证（ABA）将这些测试明确为可废止的承诺，并提供与其中一个子集的对应保证，但其现有的修复程序以启发式方式发布事实，且对用户针对发布内容的质疑不提供正式回应。

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).

我们认为，可争议的因果发现需要一个可检查的修复对象、对其的辩护，以及针对此类质疑的可预测修订程序。因此，我们将 CI 事实修复表述为一个最小成本修正问题，并在答案集编程（ASP）中实现它。

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.

基于因果 ABA，我们提供了一种紧凑且高效的因果一致性 ASP 编码，具有可靠性和完备性保证，并在最优稳定模型与最小成本修正集之间建立了双向投影对应关系。这些结果确立了对保留事实的忠实度、修复的子集最小性和成本最优性、针对已发布事实的基于冲突的辩护，以及在用户质疑下形式上可保证的补救措施。

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.

实证评估支持了这一形式化说明：OptABA-PC 在常见基准测试中保留了更多正确的事实，减少了兼容输出中的歧义，并改进了优于因果 ABA 的图重构效果。