Causal Inference via Conditional Kolmogorov Complexity using MDL Binning
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Recent developments have linked causal inference with Algorithmic Information Theory, and methods have been developed that utilize Conditional Kolmogorov Complexity to determine causation between two random variables. However, past methods that utilize Algorithmic Information Theory do not provide off-the-shelf support for continuous data types and thus lose the essence of the shape of data. We present a method for inferring causal direction between continuous variables by using an MDL Binning technique for data discretization and complexity calculation. Our method captures the shape of the data and uses it to determine which variable has more information about the other. Its high predictive performance and robustness is shown on several real world use cases.
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