INDUCED COVARIANCE FOR CAUSAL DISCOVERY IN LINEAR SPARSE STRUCTURES

Saeed Mohseni-Sehdeh, Walid Saad

Keywords: Causal discovery, Induced covariance, Sparse causal structure

Abstract:

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 Sparse Linear Causal Discovery (SLCD), 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 identifies the correct structural matrix by evaluating how well it reconstructs the data and how closely it satisfies the imposed statistical constraints.
This method does not rely on independence tests or graph fitting procedures, making it suitable for scenarios with limited training data.
Simulation results on synthetically generated datasets with known linear sparse causal structures show that SLCD consistently outperforms the PC, GES, BIC exact search, and LiNGAM-based methods, achieving average improvements of \(35\%\) in precision and \(41.5\%\) in recall. Moreover, on the real-world Sachs dataset, SLCD further surpasses these methods in the low-sample-size setting.

Primary Area: causal reasoning

Submission Number: 22220