Reconstructing regime-dependent causal relationships from observational time series

TL;DR

This paper introduces Regime-PCMCI, a method combining causal discovery with regime detection to identify regime-dependent causal relations in time series data, applicable to both simulated and real-world datasets.

Contribution

The paper presents a novel approach that integrates regime learning with PCMCI for causal discovery, addressing the challenge of regime-dependent causal relations in time series.

Findings

Regime-PCMCI can distinguish different causal directions and effects across regimes.

The method successfully detects changes in autocorrelation and causal sign shifts.

Applied to climate data, it demonstrates practical effectiveness in real-world scenarios.

Abstract

Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this difficult task. In this paper we focus on an important challenge that is at the core of time series causal discovery: regime-dependent causal relations. Often dynamical systems feature transitions depending on some, often persistent, unobserved background regime, and different regimes may exhibit different causal relations. Here, we assume a persistent and discrete regime variable leading to a finite number of regimes within which we may assume stationary causal relations. To detect regime-dependent causal relations, we combine the conditional independence-based PCMCI method with a regime learning optimisation approach. PCMCI allows for linear and nonlinear, high-dimensional time series causal discovery. Our method, Regime-PCMCI, is evaluated on a number of numerical experiments demonstrating that it can distinguish regimes with different causal directions, time lags, effects and sign of causal links, as well as changes in the variables' autocorrelation. Further, Regime-PCMCI is employed to observations of El Ni\~no Southern Oscillation and Indian rainfall, demonstrating skill also in real-world datasets.