SURD: A causal Inference tool for Scientific Discovery | 9am PT, Tues, Feb 4, 2025

[Grigory Bronevetsky]

Modeling Talks

SURD: A causal Inference tool for Scientific Discovery  Adrian Lozano Duran, MIT Tues, Feb 4, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/surd-a-causal-inference-tool-for-scientific-discovery More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home 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.

[Grigory Bronevetsky]

Video Recording: https://www.youtube.com/watch?v=DRUVXNnSqaE
Slides: https://drive.google.com/file/d/1XYgq78uY-PC64GGvr5d_aK-C4mcZCwSm/view

Summary:

• Focus: causal discovery in chaotic systems

  • Information theoretic

  • Causal inference

  • Application: decomposition of turbulent flow fields

• Causality analysis pioneered by economics community

• Goal: identify causal relationships among attributes of a system

  • Causal: which attributes influence the future evolution of the physical system

• Causal analysis of high-dimensional chaotic systems

  • Physical insight

  • Causality-preserving reduced order models: small model captures the causal drivers encoded in the full system

  • Causality-driven control: indicate what needs to be changed to produce a given outcome

• Casualty:

  • Causes should precede effect

  • Causality is different from correlation & correlation

  • Emergent macroscopic quantity

• Challenges

  • Mediation: Q3->Q2->Q1

  • Confounder: Q3->Q2, Q3->Q1

    • One cause has multiple effects

    • The effects are correlated but not causally related to each other

  • Synnergistic collider: Q3->Q2, Q3->Q1

    • Combination of multiple causes produces novel effects

  • Redundant collider: Q3->Q1, Q2->Q1, Q3<->Q2

    • Multiple causes independently cause the same effect

  • Self-causation: Q1->Q1

    • Past values of variable cause future values

  • Noise

  • Exogenous/unobserved factors

• Interventions are the best way to probe causality but it is challenging in practice

  • Impossible to intervene in the past

  • May be unethical (e.g. human studies)

  • Systems are often operating in their natural attractor and it is hard to push them out

    • Which interventions are meaningful for establishing causality? 

    • Synergistic and redundant colliders force us to do many interventions to probe their structure

• Observational methods can be applied much more generally

  • Model Forecasting

  • Statistical independence relations

  • Randomized experiments

  • Information-theoretic methods

  • Attractor reconstruction

• Approach: SURD: Synnergistic, unique, redundant decomposition of causality

  • Causality is increase in information about the target variable (Shannon entropy)

  • Forward-in-time propagation of information

  • Goal: linearly decompose into causality components

    • Synnergistic: Join effect from multiple variables

    • Unique: causality from a var that cannot be obtained from other vars

    • Redundant: common to a group of vars

    • Leak: causality from unobserved vars

    • Method ensures these sources of causality add up to 1

• Application: wind blowing over water, generating waves

  • To what extent does the wind drive the water and to what extent do the water waves affect the wind?

  • Data: experimental dataset of wind/water motion

  • SURD indicates that water does not drive the wind but there is a small redundancy between the two