新方法可同时处理因果循环和测量噪声，提升真实数据的因果推断准确率。
RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
- 基于期望最大化算法，用残差归一化流计算观测数据似然，建模复杂因果结构。
- 在合成数据和蛋白质信号真实数据上均优于现有方法，显著降低错误连接率。
- 适合基因调控网络、生物信号通路等存在环状依赖与测量误差的研究场景。
揭示因果关系是科学与工程中的基础问题。然而，大多数现有因果发现方法假设因果图无环且能直接观测系统变量——这些假设在许多真实场景中不成立。例如，在基因组学中，环状调控网络普遍存在，且测量常受仪器噪声干扰。为此，我们提出RECLAIM，一种原生支持循环结构与测量噪声的因果发现框架。RECLAIM通过期望最大化（EM）算法，最大化观测数据的似然来学习因果图结构，并采用残差归一化流实现高效的似然计算。我们考虑两种测量模型：(i) 高斯加性噪声；(ii) 线性测量系统加高斯噪声。针对两种设定，我们提供了理论一致性保证。在合成数据与真实世界蛋白质信号数据集上的实验表明，该方法具有优异性能。
原文摘要 · Abstract (English)
Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the system variables -- assumptions that fail to hold in many real-world settings. For instance, in genomics, cyclic regulatory networks are common, and measurements are often corrupted by instrumental noise. To address these challenges, we propose RECLAIM, a causal discovery framework that natively handles both cycles and measurement noise. RECLAIM learns the causal graph structure by maximizing the likelihood of the observed measurements via expectation-maximization (EM), using residual normalizing flows for tractable likelihood computation. We consider two measurement models: (i) Gaussian additive noise, and (ii) a linear measurement system with additive Gaussian noise. We provide theoretical consistency guarantees for both the settings. Experiments on synthetic data and real-world protein signaling datasets demonstrate the efficacy of the proposed method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。