Causal Inference via Conditional Kolmogorov Complexity using MDL Binning
This work addresses causal inference for researchers in statistics and machine learning, presenting an incremental improvement by applying MDL binning to an existing algorithmic information theory approach.
The paper tackles the problem of inferring causal direction between continuous variables by introducing a method that uses MDL binning for data discretization and complexity calculation, achieving high predictive performance and robustness on real-world use cases.
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. 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.