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The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity.
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Causal Inference Using the Algorithmic Markov Condition
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A Linear Non-Gaussian Acyclic Model for Causal Discovery
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Causal Discovery with Continuous Additive Noise Models
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