新方法用信息论识别隐变量影响下的因果关系。
MDL Meets Latent Confounders: LNML-based Causal Discovery
- 基于最小描述长度原理，通过压缩编码长度判断因果方向。
- 在合成与真实数据上准确恢复因果结构并检测隐变量。
- 适合处理非线性关系和隐变量混杂的复杂场景。
在非线性机制和隐变量混杂的条件下进行因果发现仍具挑战性。现有方法通常依赖线性假设或因果充分性，限制了适用范围。本文提出一种基于最小描述长度（MDL）的因果发现框架，通过最小化幸运性归一化最大似然（LNML）编码长度，显式考虑隐变量影响，同时支持灵活的非线性机制。通过比较每对变量间因果模型的最短编码长度来确定因果关系，并引入Δ-伪共线性概念识别由隐变量引起的依赖关系。基于此，我们设计了一种贪心算法——伪共线性引导因果发现（PCG-CD）。在合成数据和真实数据集上的实验表明，该方法能准确恢复有向因果关系，并有效检测隐变量。
原文摘要 · Abstract (English)
Causal discovery with nonlinear mechanisms and latent confounders remains challenging. Existing methods often rely on either linear assumptions or causal sufficiency, limiting their applicability. We propose an MDL-based causal discovery framework that explicitly accounts for latent confounders while allowing flexible nonlinear mechanisms by minimizing the luckiness normalized maximum likelihood (LNML) code-length. The causal relationship between each variable pair is determined by selecting the shortest code-length of the causal model, and we introduce the notion of $Δ$-pseudo-collinearity to identify dependencies induced by latent confounders. Based on these ideas, we develop a greedy algorithm, termed Pseudo-Collinearity Guided Causal Discovery (PCG-CD). Experiments on synthetic and real-world datasets demonstrate that the proposed method accurately recovers directed causal relationships and effectively detects latent confounders.
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。