2022/04/25 by Aditi Kathpalia, Kathpalia, Aditi, Pouya Manshour +3 · 1 citation
Computer Science · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Neural Networks and Applications #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting #physics.data-an
paper · pdf · doi:10.48550/arxiv.2204.11731
14 pages, 3 figures, 1 table
arxiv created 2022/04/25 · openalex publication_date 2022/04/25 · arxiv updated 2022/04/26 · openalex created_date 2022/04/28 · openalex updated_date 2026/07/28
Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener-Granger's idea. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. However, it only works for one-dimensional time series. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC), which retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially hidden variables. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples.