351 citations · 1.2k across the 48 of their papers we have counts for
5 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…
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…
Testing whether linear equations are causal: A free probability theory approach
Jakob Zscheischler, Dominik Janzing, Kun Zhang
We propose a method that infers whether linear relations between two high-dimensional variables X and Y are due to a causal influence from X to Y or from Y to X. The earlier propos…
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…