<ul><li>Identifying causal direction between variables using observational data is challenging in various scientific disciplines.</li><li>Algorithmic Markov condition is a key principle used in determining causal direction based on codelengths.</li><li>Proposed method leverages variational Bayesian learning of neural networks to enhance model fitness and promote succinct codelengths.</li><li>Experiments show the effectiveness of the method in cause-effect identification, outperforming other approaches in both synthetic and real-world scenarios.</li></ul>

Identifying Causal Direction via Variational Bayesian Compression

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