Decomposing causality into its synergistic, unique, and redundant components

Nature Communications volume  15, Article number: 9296 (2024 ) Cite this article

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.

The quest for understanding causality is the cornerstone of scientific discovery1. It is through the exploration of cause-and-effect relationships that we are able to understand a given phenomenon and shape the course of events through deliberate actions2. This has accelerated the proliferation of methods for causal inference, as they hold the potential to drive progress across multiple scientific and engineering domains, such as climate research3, neuroscience4, economics5, epidemiology6, social sciences7, and fluid dynamics8,9, among others.

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