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Found 4 papers, 1 papers with code
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Inferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance.
Causal discovery in a complex industrial system: A time series benchmark
Causal discovery outputs a causal structure, represented by a graph, from observed data.
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered.
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i. i. d.
Model-based Reinforcement Learning
reinforcement-learning
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