Multiscale Non-stationary Causal Structure Learning from Time Series Data. (arXiv:2208.14989v1 [cs.LG])

This paper introduces a new type of causal structure, namely multiscale
non-stationary directed acyclic graph (MN-DAG), that generalizes DAGs to the
time-frequency domain. Our contribution is twofold. First, by leveraging
results from spectral and causality theories, we expose a novel probabilistic
generative model, which allows to sample an MN-DAG according to user-specified
priors concerning the time-dependence and multiscale properties of the causal
graph. Second, we devise a Bayesian method for the estimation of MN-DAGs, by
means of stochastic variational inference …

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