Discovering Causal Structure with Reproducing-Kernel Hilbert Space $\epsilon$-Machines

TL;DR

This paper introduces a novel method combining computational mechanics and reproducing-kernel Hilbert space techniques to infer causal structures directly from observational data, applicable to both discrete and continuous systems.

Contribution

It presents a new approach to infer causal states and their topology using RKHS, enabling efficient modeling of complex system dynamics through kernel $mbda$-machines.

Findings

Successfully applied to infinite Markov-order processes and chaotic flows.

Robustly estimates causal structure despite noise and high dimensionality.

Predicts system behavior using a derived evolution operator.

Abstract

We merge computational mechanics' definition of causal states (predictively-equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely-applicable method that infers causal structure directly from observations of a system's behaviors whether they are over discrete or continuous events or time. A structural representation -- a finite- or infinite-state kernel $\epsilon$-machine -- is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker-Plank equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably-infinite causal states and (iii) continuous-time, continuous-value processes generated by thermally-driven chaotic flows. The method robustly estimates causal structure in the presence of varying external and measurement noise levels and for very high dimensional data.