Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework

📰 ArXiv cs.AI

Learn how to apply Quantitative Argumentation for Causal Discovery (QACD) to improve causal discovery in finite-sample regimes

advanced Published 28 Apr 2026

Action Steps

Map statistical test outcomes to argument strengths using QACD
Aggregate conflicted arguments to determine causal relationships
Apply QACD to finite-sample regimes to reduce structural errors
Compare QACD results with traditional constraint-based causal discovery methods
Configure QACD parameters to optimize causal discovery performance

Who Needs to Know This

Data scientists and researchers working on causal discovery problems can benefit from this framework to improve the accuracy of their models

Key Insight

💡 QACD represents conditional-independence outcomes as graded, defeasible arguments to reduce errors in causal discovery

Key Takeaways

Learn how to apply Quantitative Argumentation for Causal Discovery (QACD) to improve causal discovery in finite-sample regimes

Full Article

Title: Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework

Abstract:
arXiv:2604.23633v1 Announce Type: new 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 conflicti

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