用可辩驳的论证框架改进因果发现，让错误不会一路传递
Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework
- 把独立性检验结果当有强度的论据，而非绝对约束
- 通过证据传播机制聚合矛盾信息，得到稳定结构判断
- 适合在数据少、检验不准时做因果推断
基于约束的因果发现方法在有限样本下易受误判影响，错误的条件独立（CI）判断会引发严重结构误差。本文提出定量论证因果发现（QACD）框架，将CI结果表示为可削弱的、带强度的论据，而非不可逆的约束。QACD将统计检验结果映射为论据强度，通过连通性驱动的见证传播聚合冲突证据，生成候选边的固定点可接受性标注。在标准贝叶斯网络基准测试中，QACD在多种噪声或不一致的CI环境下均提升了结构一致性与干预可靠性，同时保持对经典约束法、混合方法及已有论证基方法的竞争力。
原文摘要 · Abstract (English)
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.
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