971 citations · 1.6k across the 23 of their papers we have counts for
7 papers · 1 filter
Causal discovery for time series from multiple datasets with latent contexts
Wiebke Günther, Urmi Ninad, Jakob Runge
Causal discovery from time series data is a typical problem setting across the sciences. Often, multiple datasets of the same system variables are available, for instance, time ser…
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
Wiebke Günther, Urmi Ninad, jonas Wahl +1
Conditional independence (CI) testing is frequently used in data analysis and machine learning for various scientific fields and it forms the basis of constraint-based causal disco…
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…
Foundations of Causal Discovery on Groups of Variables
Jonas Wahl, Urmi Ninad, Jakob Runge
Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models.…
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…