SOURCE-LINKED INTELLIGENCE

Reliable Cause-Effect Quantification in Complex Dynamical Systems for Socially Beneficial Policies

CORDIS · observation · Publication date unknown

g of 'if-then' relationships in complex spatio-temporal systems. Traditional dynamical models are limited by oversimplifications and computational constraints, while powerful hybrid models leveraging machine learning (ML) lack the interpretability and generalization capabilities essential for entrusting them with policy guidance. Filling this glaring research gap, DYNAMICAUS develops novel methods for reliable causal inference in such systems, overcoming existing limitations. I will establish a theoretical foundation for causal effects in hybrid dynamical models, develop robust uncertainty quantification techniques under unseen interventions augmented by active data acquisition to reduce uncertainty, and quantify the overall impact of steerable inputs on target outcomes. My methods will be validated across critical domains – climate modeling, treatment planning, and epidemic simulations – demonstrating universal applicability and profound potential for informing policy on pressing societal challenges. By integrating an ethicist directly into the research process from the outset, I pr

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR