# Causal Identification with Additive Noise Models: Quantifying the Effect of Noise

@article{Kap2021CausalIW, title={Causal Identification with Additive Noise Models: Quantifying the Effect of Noise}, author={Benjamin Kap and Marharyta Aleksandrova and Thomas Engel}, journal={ArXiv}, year={2021}, volume={abs/2110.08087} }

In recent years, a lot of research has been conducted within the area of causal inference and causal learning. Many methods have been developed to identify the cause-effect pairs in models and have been successfully applied to observational real-world data to determine the direction of causal relationships. Yet in bivariate situations, causal discovery problems remain challenging. One class of such methods, that also allows tackling the bivariate case, is based on Additive Noise Models (ANMs…

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