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From Deterministic ODEs to Dynamic Structural Causal Models
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Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood. In this paper we provide a novel perspective on the relationship between Ordinary Differential Equations and Structural Causal Models. We show how, under certain conditions, the asymptotic behaviour of an Ordinary Differential Equation under non-constant interventions can be modelled using Dynamic Structural Causal Models. In contrast to earlier work, we study not only the effect of interventions on equilibrium states; rather, we model asymptotic behaviour that is dynamic under interventions that vary in time, and include as a special case the study of static equilibria.
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Cited by 1 Pith paper
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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems
A deep Koopman framework called Kausal discovers causal direction and magnitude in nonlinear dynamical systems by comparing joint versus marginal prediction errors in learned observable spaces.
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