Author:
Kathpalia Aditi,Manshour Pouya,Paluš Milan
Abstract
AbstractDistinguishing 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). We demonstrate that PCCC retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially unobserved variables which can be reconstructed using the embedding theorem. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples.
Funder
Czech Science Foundation
Czech Academy of Sciences
Publisher
Springer Science and Business Media LLC
Reference115 articles.
1. Pearl, J. & Mackenzie, D. The Book of Why: The New Science of Cause and Effect (Basic Books, 2018).
2. Kathpalia, A. & Nagaraj, N. Measuring causality. Resonance 26, 191 (2021).
3. Wiener, N. The theory of prediction. Mod. Math. Eng. 1, 125–139 (1956).
4. Granger, C. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969).
5. Geweke, J. Inference and causality in economic time series models. Handb. Econom. 2, 1101–1144 (1984).
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