Robust Causal Discovery via Abductive Quantitative Argumentation

Constraint-based causal discovery is notoriously fragile under finite-sample errors, which often trigger devastating structural error cascades. To address this, we propose Abductive Quantitative Argumentation for Causal Discovery (AQACD), a probabilistic-symbolic framework that reformulates causal learning as abductive argumentation. Unlike deterministic algorithms or exact logic solvers, AQACD models causal relations as defeasible hypotheses, dynamically evaluated through a continuous dialectic network of statistical and structural arguments. Rather than making irreversible commitments, graph revisions are guided by Inference to the Best Explanation: the topology is updated only when an alternative explanation decisively outweighs its rivals under a dialectic margin, rescuing mistakenly pruned edges (${\tau }_{\text{triad}}$). This approach strictly improves global internal consistency while bypassing the exponential bottlenecks typical of exact logical inference. Extensive experiments on standard benchmarks demonstrate that AQACD consistently outperforms classical and continuous baselines. Most notably, it achieves substantial gains in orientation accuracy (Arrow F1) within Markov Equivalence Classes, exhibiting exceptional robustness to noise and neutralizing error cascades.