SURD: A causal Inference tool for Scientific Discovery
Adrian Lozano Duran, MIT
https://aeroastro.mit.edu/people/adri...
Slides and Summary: https://sites.google.com/modelingtalk...
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.
Bio:
Adrian Lozano Duran is an Associate Professor at GALCIT, Caltech, and a Visiting Associate Professor at MIT AeroAstro. He received his Ph.D. in Aerospace Engineering from the Technical University of Madrid in 2015. From 2016 to 2020, he was a Postdoctoral Research Fellow at Stanford University's Center for Turbulence Research. He served as an Assistant Professor at MIT from 2021 to 2024. His research focuses on computational fluid mechanics and the physics of turbulence, including causal inference, modeling, and control of turbulence using information theory. He is also interested in developing closure models for large-eddy simulations of aerospace applications using artificial intelligence.
#modeling #simulation #causalinference #ai #ml #rootcauses #prediction #forecasting #datascience #sciml