Assimilative Causal Inference

TL;DR

This paper introduces assimilative causal inference (ACI), a novel Bayesian data assimilation framework that identifies dynamic causal relationships in complex, high-dimensional systems, even with limited data and intermittent causal roles.

Contribution

ACI is a new methodological framework that traces causes backward from effects, accommodating high-dimensional data and providing online, reversible causal tracking.

Findings

Successfully applied to complex dynamical systems with intermittency.

Provides a rigorous criterion for causal influence range.

Demonstrates effectiveness in systems with extreme events.

Abstract

Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems. In this paper, assimilative causal inference (ACI) is developed, which is a methodological framework that leverages Bayesian data assimilation to trace causes backward from observed effects. ACI solves the inverse problem rather than quantifying forward influence. It uniquely identifies dynamic causal interactions without requiring observations of candidate causes, accommodates short datasets, and, in principle, can be implemented in high-dimensional settings by employing efficient data assimilation algorithms. Crucially, it provides online tracking of causal roles that may reverse intermittently and facilitates a mathematically rigorous criterion for the causal influence range, revealing how far effects propagate. The effectiveness of ACI is demonstrated by complex dynamical systems showcasing intermittency and extreme events. ACI opens valuable pathways for studying complex systems, where transient causal structures are critical.