提出新方法在稀疏线性结构中高效发现因果关系。
Induced Covariance for Causal Discovery in Linear Sparse Structures
- 基于结构矩阵重构数据并利用其统计特性识别因果结构。
- 在有限数据下优于PC、GES、BIC和LINGAM等经典方法。
- 无需独立性检验，适合小样本场景，适合因果推断研究者。
因果模型旨在从观测数据中揭示变量间的因果关系，而非传统回归模型仅描述变量间映射关系。本文提出一种新型因果发现算法，适用于变量间呈线性稀疏关系的场景。在此类情况下，因果链接可由有向无环图（DAG）表示，并编码于结构矩阵中。所提方法利用结构矩阵的数据重构能力及其对数据施加的统计特性，以识别正确的结构矩阵。该方法不依赖独立性检验或图拟合过程，因此适用于训练数据有限的情形。仿真结果表明，该方法在恢复线性稀疏因果结构方面优于著名的PC、GES、BIC精确搜索及基于LINGAM的方法。
原文摘要 · Abstract (English)
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.
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