Learning soft interventions in complex equilibrium systems


Complex systems often contain feedback loops that can be described as cyclic causal models. Intervening in such systems may lead to counter-intuitive effects, which cannot be inferred directly from the graph structure. After establishing a framework for differentiable interventions based on Lie groups, we take advantage of modern automatic differentiation techniques and their application to implicit functions in order to optimize interventions in cyclic causal models. We illustrate the use of this framework by investigating scenarios of transition to sustainable economies.

In UAI 2022 (accepted)
Source Themes
Michel Besserve
Research Group leader

My research interests include neuroscience, complex systems, machine learning and causality.