2016/11/01 by Aleksander Wieczorek, Wieczorek, Aleksander, Volker Röth +2
Computer Science · Engineering · Mathematics · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (stat.ML) #Machine Learning and Algorithms #stat.ML
paper · pdf · doi:10.48550/arxiv.1611.00261
arxiv created 2016/11/01 · openalex publication_date 2016/11/01 · arxiv updated 2016/11/02 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28
We propose a new method of discovering causal relationships in temporal data based on the notion of causal compression. To this end, we adopt the Pearlian graph setting and the directed information as an information theoretic tool for quantifying causality. We introduce chain rule for directed information and use it to motivate causal sparsity. We show two applications of the proposed method: causal time series segmentation which selects time points capturing the incoming and outgoing causal flow between time points belonging to different signals, and causal bipartite graph recovery. We prove that modelling of causality in the adopted set-up only requires estimating the copula density of the data distribution and thus does not depend on its marginals. We evaluate the method on time resolved gene expression data.