351 citations · 602 across the 6 of their papers we have counts for
4 papers · 1 filter
On Causal and Anticausal Learning
Bernhard Schoelkopf, Dominik Janzing, Jonas Peters +3
We consider the problem of function estimation in the case where an underlying causal model can be inferred. This has implications for popular scenarios such as covariate shift, co…
Kernel-based Conditional Independence Test and Application in Causal Discovery
Kun Zhang, Jonas Peters, Dominik Janzing +1
Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery. Due to the curse of dimensionality, testing for conditional…
Identifiability of Causal Graphs using Functional Models
Jonas Peters, Joris Mooij, Dominik Janzing +1
This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by…
Detecting low-complexity unobserved causes
Dominik Janzing, Eleni Sgouritsa, Oliver Stegle +2
We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a "direct" causal link or only due to a connecting causal path th…