no code implementations • 17 Oct 2023 • Oana-Iuliana Popescu, Andreas Gerhardus, Jakob Runge
One approach computes distances by a one-hot encoding of the categorical variables, essentially treating categorical variables as discrete-numerical, while the other expresses CMI by entropy terms where the categorical variables appear as conditions only.
no code implementations • 9 Oct 2023 • Andreas Gerhardus, jonas Wahl, Sofia Faltenbacher, Urmi Ninad, Jakob Runge
In this work, we develop a method for projecting infinite time series graphs with repetitive edges to marginal graphical models on a finite time window.
1 code implementation • 15 Jun 2023 • Kevin Debeire, Jakob Runge, Andreas Gerhardus, Veronika Eyring
It can be combined with a range of time series causal discovery methods and provides a measure of confidence for the links of the time series graphs.
no code implementations • 21 May 2023 • Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.
1 code implementation • 11 Apr 2023 • Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam, Milton S. Gomez, Jakob Runge, Andreas Gerhardus
We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging).
no code implementations • 15 Dec 2021 • Andreas Gerhardus
In this paper, we introduce a novel class of graphical models for representing time lag specific causal relationships and independencies of multivariate time series with unobserved confounders.
1 code implementation • NeurIPS 2020 • Andreas Gerhardus, Jakob Runge
We show that existing causal discovery methods such as FCI and variants suffer from low recall in the autocorrelated time series case and identify low effect size of conditional independence tests as the main reason.