Decomposing causality into its synergistic, unique, and redundant components

TL;DR

This paper introduces SURD, a novel method for decomposing causality into synergistic, unique, and redundant components, effectively addressing complex challenges in causal inference across various systems.

Contribution

SURD provides a comprehensive, non-intrusive framework for quantifying causality components, integrating multiple causal complexities into a unified approach.

Findings

SURD outperforms existing methods in challenging causal inference scenarios.

It reliably quantifies causality even with scarce data.

SURD effectively decomposes causality into synergistic, unique, and redundant parts.

Abstract

Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to nonlinear dependencies, stochastic interactions, self-causation, collider effects, and influences from exogenous factors, among others. While existing methods can effectively address some of these challenges, no single approach has successfully integrated all these aspects. Here, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant, unique, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods.