Causal Inference via Conditional Kolmogorov Complexity using MDL Binning
Machine Learning
2019-11-11 v2 Information Theory
math.IT
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1911.00332,
title = {Causal Inference via Conditional Kolmogorov Complexity using MDL Binning},
author = {Daniel Goldfarb and Scott Evans},
journal= {arXiv preprint arXiv:1911.00332},
year = {2019}
}