CSE Community Seminar
April 12, 2024, 12 PM
Conference Room 45-432 in Building 45
Causal inference in complex physical systems
Adrian Lozano-Duran
Draper Assistant Professor,
Aeronautics and Astronautics, MIT
Abstract:
Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding the interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to the presence of nonlinear dependencies, stochastic and deterministic interactions, self-causation, mediator, confounder, and collider effects, and contamination from unobserved, exogenous factors, to name a few. While there are methods that can effectively address some of these challenges, no single approach has been successful in integrating all these aspects. Here, we tackle 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 based on available information from past observations. The formulation is non-intrusive and requires only pairs of past and future events, facilitating its application in both computational and experimental investigations, even when samples are scarce. We benchmark SURD against existing methods in scenarios that pose significant challenges in causal inference. These include synchronization in logistic maps, the Rössler-Lorenz system, the Lotka-Volterra prey-predator model, the Moran effect model, and energy cascade in turbulence, among others. Our findings demonstrate that SURD offers a more reliable quantification of causality compared to state-of-the-art methods for causal inference.
April 12, 2024 CSE Community Seminar
Adrian Lozano-Duran
Draper Assistant Professor
Aeronautics and Astronautics, MIT