将带噪成对知识耦合到DAG后验以实现因果发现
Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery
- Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出HB-NoisyKG贝叶斯框架，将大语言模型等来源的带噪因果报告与观测数据结合，通过交替推断细化可靠性，在多个基准上显著降低SHD并提升AUROC。
AI中文摘要：
外部因果报告可以改善有限观测下的结构学习，但其可靠性在不同来源和变量对之间存在差异。我们提出HB-NoisyKG，一个贝叶斯框架，将观测数据与来自大语言模型等来源的重复因果报告相结合。每条报告是对一个由某个DAG隐含的直接对状态的带噪观测。一个以特征为条件的Beta先验汇集了关于对可靠性的信息，一个共享的错误矩阵捕获系统性错误。交替推断利用图后验来细化可靠性估计，而可靠性估计决定了报告如何影响后续的图更新。报告似然仅使用图的成对状态边际，因此相同的观测层在仅图推断和联合推断中支持离散和连续似然。相对于80次重启的无知识基线，HB最多使用总共80次重启，并在五个离散基准上将平均结构汉明距离（SHD）从22.39降至16.06。在一个具有随机变量ID和保留描述的物理光隧道上，HB将SHD从无知识时的39.00降至27.30。在连续Sachs数据上，仅图BGe将AUROC比无知识Top-K提高了0.121。在一项受控合成研究中，与一次性估计相比，持续更新还降低了平均可靠性估计误差和留出报告对数损失。
英文摘要：
External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We introduce HB-NoisyKG, a Bayesian framework that combines observational data with repeated causal reports from sources such as large language models. Each report is a noisy observation of a direct pair state implied by one DAG. A feature-conditioned Beta prior pools information about pair reliability, and a shared error matrix captures systematic mistakes. Alternating inference uses the graph posterior to refine reliability estimates, which determine how reports influence subsequent graph updates. The report likelihood uses only graph pair-state marginals, so the same observation layer supports discrete and continuous likelihoods in graph-only and joint inference. Against an 80-restart no-KG baseline, HB uses at most 80 total restarts and lowers mean SHD from 22.39 to 16.06 on five discrete benchmarks. On a physical light tunnel with random variable IDs and retained descriptions, HB lowers SHD from 39.00 for no-KG to 27.30. On continuous Sachs, graph-only BGe raises AUROC by 0.121 over no-KG Top-K. In a controlled synthetic study, continued updating also reduces mean reliability estimation error and held-out report log loss compared with one-time estimation.
