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stat.ME2023
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…
stat.ME2023
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.…
stat.ME2022
Vector causal inference between two groups of variables
Jonas Wahl, Urmi Ninad, Jakob Runge
Methods to identify cause-effect relationships currently mostly assume the variables to be scalar random variables. However, in many fields the objects of interest are vectors or g…