Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework

arXiv:2604.23633 · cs.AI · Submitted 2026-04-26 · Read on arXiv

cs.AI

Submitted: 2026-04-26

Updated: 2026-04-26

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

DOI: 10.24963/kr.2026/63

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Causal Discovery — 3× in this paper · explained in 3 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Quantitative Argumentation for Causal Discovery — 1× in this paper

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• Agents' Last Exam
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems