55 citations · 145 across the 14 of their papers we have counts for
4 papers · 2 filters
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.…