Constraint‑based causal discovery relies on conditional‑independence (CI) tests that may conflict when only a finite sample is available, leaving no causal graph that satisfies all tests. Causal Assumption‑based Argumentation (ABA) makes these tests explicit as defeasible commitments and offers correspondence guarantees for a subset of them, but its current repair procedure releases facts heuristically and lacks a 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. Accordingly, we 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 ensure 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 shows that on common benchmarks OptABA‑PC retains more correct facts, reduces ambiguity in compatible outputs, and improves graph reconstruction compared with the original Causal ABA.
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