Optimal correction sets for argumentative causal discovery
File(s)
Accepted version
Author(s)
Russo, Fabrizio
Mazzotta, Giuseppe
Dodaro, Carmine
Ricca, Francesco
Toni, Francesca
Type
Conference Paper
Abstract
Causal Assumption-based Argumentation (ABA) has been proposed as a causal discovery method with increased guarantees on the correspondence of the discovered causal graphs to a subset of the input constraints that drive the search for the causal relations. Heuristics are currently used to identify the optimal subset of constraints and infer the most likely corresponding graphs. Minimal Unsatisfiable Sets (MUSes) and Minimal Correction Sets (MCSes) have been explored in the Answer Set Programming (ASP) literature to create explanations and repair logic programs (LPs). We leverage the correspondence of stable semantics between ABA and LPs to investigate the benefits and drawbacks of MUSes and MCSes when applied to the causal discovery task carried out by Causal ABA. We define the notion of optimal MCSes and show how they can be computed by leveraging standard optimisation constructs such as weak constraints. We then empirically show that optimal MCSes, integrated into Causal ABA for causal discovery, substantially (i) increase the identification rate of true constraints; (ii) reduce the number of compatible causal graphs in output; (iii) improve graph reconstruction according to standard metrics.
Date Acceptance
2026-05-22
Copyright Statement
© 2026 The Author(s). This paper is embargoed until publication.
Source
Logical approaches to handling INconsistent DAta (LINDA 2026) co-located with the Conference on Principles of Knowledge Representation and Reasoning (KR’2026) as a part of the Federated Logic conference (FLoC’2026)
Publication Status
Accepted
Start Date
2026-07-24
Coverage Spatial
Lisbon, Portugal