基于因果效应约束，可解释地发现系统中隐藏的因果关系。
Interpretable Causal Discovery via Causal-Effect Constraints
- 将因果发现建模为贝叶斯推断，引入因果效应约束条件。
- 在小概率事件下仍能准确估计因果图与参数，支持大规模数据。
- 适用于生物通路等科学探索，提供可解释的路径级分析结果。
因果发现旨在从系统生成的数据中揭示潜在的因果关系。其目标不仅是预测因果边，还需解释观测或假设现象（如特别大的因果效应）。本文关注条件因果发现，将其建模为贝叶斯推断问题，目标是计算在特定事件（如因果效应约束）条件下因果图与参数的后验分布。然而，这带来计算挑战：现有方法在事件后验概率较小时表现不佳。为此，我们引入罕见事件估计技术，在联合图-参数空间中进行推断，通过渐进引导粒子群体逼近约束区域，同时保持对条件后验的良好近似。在合成图上的实证评估验证了该方法在小规模与大规模场景下的准确性；在Sachs蛋白数据集的案例研究中，展示了其如何辅助科学探索，提供通路级别的总结分析。
原文摘要 · Abstract (English)
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.
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