Causal models seek to unravel the cause-effect relationships among variables from observed data, as opposed to mere mappings among them, as traditional regression models do. This paper introduces a novel causal discovery algorithm designed for settings in which variables exhibit linearly sparse relationships. In such scenarios, the causal links represented by directed acyclic graphs (DAGs) can be encapsulated in a structural matrix. The proposed approach leverages the structural matrix's ability to reconstruct data and the statistical properties it imposes on the data to identify the correct structural matrix. This method does not rely on independence tests or graph fitting procedures, making it suitable for scenarios with limited training data. Simulation results demonstrate that the proposed method outperforms the well-known PC, GES, BIC exact search, and LINGAM-based methods in recovering linearly sparse causal structures.

本研究解决了因果建模中变量间线性稀疏关系的识别问题。提出了一种新颖的因果发现算法，这种算法依赖于结构矩阵的重构能力和统计属性，以确定正确的结构矩阵。研究结果表明，该方法在恢复线性稀疏因果结构方面超越了现有的其他方法，具有显著的应用潜力。

线性稀疏结构中的因果发现的诱导协方差