An Information Theoretic Learning for Causal Direction Identification
Hang Wu, May Dongmei Wang · 2020
Causal inference has been one of the central problems in many data science research and one of the most important problem in causal inference is to draw causal conclusions using observation data. In this paper, we focus on learning causal relationships between variables using observation data. We proposed novel scoring method based on mutual information and corresponding learning algorithms. We then showed the consistency of our models under mild conditions and also validated the effectiveness of our approach in benchmarking experiments.