PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples. 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. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC). However, it only works for one-dimensional time series. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener–Granger’s idea. Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science.

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