MIT News  November 1, 2024 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. A team of researchers in the US (MIT, Caltech) developed SURD: Synergistic-Unique-Redundant Decomposition of causality which quantified causality as the increments of redundant, unique, and synergistic information gained about future events from past observations. The formulation was non-intrusive and applicable to both computational and experimental investigations, even when […]