# Causal Discovery in Linear Structural Causal Models with Deterministic Relations

@article{Yang2021CausalDI, title={Causal Discovery in Linear Structural Causal Models with Deterministic Relations}, author={Yuqin Yang and Mohamed S. Nafea and AmirEmad Ghassami and Negar Kiyavash}, journal={ArXiv}, year={2021}, volume={abs/2111.00341} }

Linear structural causal models (SCMs)– in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources– are pervasive in causal inference and casual discovery. However, for the task of causal discovery, existing work almost exclusively focus on the submodel where each observed variable is associated with a distinct source with non-zero variance. This results in the restriction that no observed variable can deterministically…

## One Citation

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