From Ordinary Differential Equations to Structural Causal Models: the deterministic case
arXiv:1408.2063
Abstract
We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM). Our exposition sheds more light on the concept of causality as expressed within the framework of Structural Causal Models, especially for cyclic models.
Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)
References in corpus (6)
- Directed Cyclic Graphical Representations of Feedback Models
- A Discovery Algorithm for Directed Cyclic Graphs
- Identifying Independencies in Causal Graphs with Feedback
- On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note)
- Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
- Learning Why Things Change: The Difference-Based Causality Learner
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- Conditional independences and causal relations implied by sets of equations
- Identifying Causal Structure in Dynamical Systems
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