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.

翻译：因果模型旨在从观测数据中揭示变量间的因果效应关系，而非如传统回归模型那样仅建立变量间的映射关系。本文提出一种新颖的因果发现算法，适用于变量呈现线性稀疏关系的场景。在此类情境中，由有向无环图（DAG）表示的因果关联可被封装在结构矩阵中。该方法利用结构矩阵重构数据的能力及其对数据施加的统计特性来识别正确的结构矩阵。此方法不依赖于独立性检验或图拟合过程，适用于训练数据有限的场景。仿真结果表明，在恢复线性稀疏因果结构方面，所提方法优于经典的PC、GES、BIC精确搜索及基于LINGAM的方法。