55 citations · 171 across the 20 of their papers we have counts for
6 papers · 1 filter
Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation
Oana-Iuliana Popescu, Andreas Gerhardus, Jakob Runge
Conditional independence testing (CIT) is a common task in machine learning, e.g., for variable selection, and a main component of constraint-based causal discovery. While most cur…
Projecting infinite time series graphs to finite marginal graphs using number theory
Andreas Gerhardus, Jonas Wahl, Sofia Faltenbacher +2
In recent years, a growing number of method and application works have adapted and applied the causal-graphical-model framework to time series data. Many of these works employ time…
Bootstrap aggregation and confidence measures to improve time series causal discovery
Kevin Debeire, Jakob Runge, Andreas Gerhardus +1
Learning causal graphs from multivariate time series is a ubiquitous challenge in all application domains dealing with time-dependent systems, such as in Earth sciences, biology, o…
Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad +7
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the p…
Causal Inference on Process Graphs, Part I: The Structural Equation Process Representation
Nicolas-Domenic Reiter, Andreas Gerhardus, Jonas Wahl +1
When dealing with time series data, causal inference methods often employ structural vector autoregressive (SVAR) processes to model time-evolving random systems. In this work, we…
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam +3
Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge…