发表机构
Yale University; University of California, Los Angeles（耶鲁大学; 加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将条件因果发现转化为贝叶斯推理问题，采用稀有事件估计技术解决小后验质量事件的计算挑战，经合成图和Sachs蛋白质数据集验证了方法的准确性与辅助科学探索的作用。
AI 中文摘要
因果发现旨在从系统生成的数据中揭示潜在的因果关系。然而，该任务的目标不仅是根据数据预测因果边，还要能够解释观察到或假设的现象，例如特别大的因果效应。我们研究条件因果发现任务，并将其转化为贝叶斯推理问题，其中我们以因果图和参数在某一事件（如因果效应约束）条件下的后验为目标。遗憾的是，这带来了计算挑战：当事件具有小后验质量时，现有贝叶斯因果发现方法难以处理。为解决此问题，我们采用稀有事件估计技术在联合图-参数空间中执行推理。我们的方法逐步将粒子群体推向约束区域，同时保持近似条件后验的样本。在合成图上的实证评估验证了我们方法在小规模和大规模下的准确性，并且我们在Sachs蛋白质数据集的案例研究中展示了该方法如何通过提供通路级别的摘要来辅助科学探索。
英文摘要
Causal discovery aims to uncover the underlying causal relationships given data generated from a system. The goal, however, is not merely to predict causal edges given data, but also to be able to interpret and explain either observed or hypothesized phenomena, such as a particularly large causal effect. We consider this task of conditional causal discovery and cast it as a Bayesian inference problem, in which we target the posterior over causal graphs and parameters conditional on an event such as a causal-effect constraint. Unfortunately, this poses a computational challenge: existing approaches to Bayesian causal discovery struggle when the event has small posterior mass. To address this, we adapt rare-event estimation techniques to perform inference the joint graph-parameter space. Our method gradually drives a particle population toward the constrained region while maintaining samples that approximate the conditional posterior. Empirical evaluation on synthetic graphs validates the accuracy of our approach at small and large scales, and we show in a case study on the Sachs protein dataset how our method can be used to aid scientific exploration by providing pathway-level summaries.
CommentsAccepted by UAI 2026
