Observational causality by states and interaction type for scientific discovery

TL;DR

This paper introduces a state-aware causal inference method that captures how causality varies with system states and distinguishes between different types of interactions, improving understanding in complex systems.

Contribution

The work presents a novel approach to quantify state-dependent causality and interaction types, validated on benchmarks and real-world climate and turbulence data.

Findings

Effectively characterizes causal influence as a function of system state

Distinguishes between redundant and synergistic interactions

Demonstrates improved causal inference in climate and turbulence systems

Abstract

Causality plays a central role in understanding interactions between variables in complex systems. These systems often exhibit state-dependent causal relationships, where both the strength and direction of causality vary with the value of the interacting variables. In this work, we introduce a state-aware causal inference method that quantifies causality in terms of information gain about future states. The effectiveness of the proposed approach stems from two key features: its ability to characterize causal influence as a function of system state, and its capacity to distinguish between redundant and synergistic interactions. The method is validated across a range of benchmark cases in which the direction and strength of causality evolve in a prescribed manner with the state of the system. We further demonstrate the applicability of our approach in two real scenarios: the interaction between motions across scales in a turbulent boundary layer, and the Walker circulation phenomenon in tropical Pacific climate dynamics. Our results show that, without accounting for state-dependent causality as well as redundant and synergistic effects, traditional approaches to causal inference may lead to incomplete or misleading conclusions.