Compression-Complexity with Ordinal Patterns for Robust Causal Inference in Irregularly-Sampled Time Series

0558249 - ÚI 2023 US eng V - Research Report
Kathpalia, Aditi - Manshour, Pouya - Paluš, Milan
Compression-Complexity with Ordinal Patterns for Robust Causal Inference in Irregularly-Sampled Time Series.
Cornell University, 2021. 14 s. arXiv.org e-Print archive, arXiv:2204.11731.
R&D Projects: GA ČR(CZ) GA19-16066S
Grant - others:AV ČR(CZ) AP1901
Program: Akademická prémie - Praemium Academiae
Institutional support: RVO:67985807
https://arxiv.org/abs/2204.11731v1

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.
Permanent Link: http://hdl.handle.net/11104/0331976