Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework

Computer Science > Artificial Intelligence arXiv:2604.23633 (cs) [Submitted on 26 Apr 2026] Title:Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework Authors:Sheng Wei, Yulin Chen, Beishui Liao View a PDF of the paper titled Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework, by Sheng Wei and 2 other authors View PDF HTML (experimental) Abstract:Constraint-based causal discovery is brittle in finite-sample regimes because erroneous conditional-independence (CI) decisions can cascade into substantial structural errors. We propose Quantitative Argumentation for Causal Discovery (QACD), a semantics-driven framework that represents CI outcomes as graded, defeasible arguments rather than irreversible constraints. QACD maps statistical test outcomes to argument strengths and aggregates conflicting evidence through connectivity-mediated witness propagation, producing a fixed-point acceptability labeling over candidate adjacencies. Experiments on standard benchmark Bayesian networks suggest that QACD improves structural coherence and interventional reliability in several noisy or inconsistent CI regimes, while remaining competitive with classical constraint-based, hybrid, and prior argumentation-based baselines. Comments: Accepted at the 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026). This arXiv version includes supplementary material and additional implementation details Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.23633 [cs.AI] (or arXiv:2604.23633v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.23633 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sheng Wei [view email] [v1] Sun, 26 Apr 2026 09:47:41 UTC (48 KB) Full-text links: Access Paper: View a PDF of the paper titled Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework, by Sheng Wei and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new…

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