Tensor-based second-order causal discovery
Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal Discovery (TSCD). Its input is a tensor obtained from the covariance matrices of observational and interventional data. Assuming the causal dependencies follow a linear structural equation model on a directed acyclic graph (DAG), TSCD outputs the DAG and the functions on its edges, requiring only that the noise variables are uncorrelated. We also implement a version of the approach for nonlinear models. Our focus on second-order statistics (via the covariance matrices) is motivated by their statistical and computational efficiency rel...
| Authors | Nathan Ouyang, Kexin Wang, Anna Seigal |
|---|---|
| Primary category | stat.ML |
| Published | 16 June 2026 |
| Last updated at source | 22 June 2026 |
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