1.6k citations · 12.4k across the 394 of their papers we have counts for
6 papers · 2 filters
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…
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
Kun Zhang, Bernhard Schoelkopf, Dominik Janzing
In nonlinear latent variable models or dynamic models, if we consider the latent variables as confounders (common causes), the noise dependencies imply further relations between th…
Inferring deterministic causal relations
Povilas Daniusis, Dominik Janzing, Joris Mooij +4
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints…
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…