most citedKernel-based Conditional Independence Test and Application in Causal Discovery

351 citations · 720 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2012244 cited

On the Identifiability of the Post-Nonlinear Causal Model

Kun Zhang, Aapo Hyvarinen

By taking into account the nonlinear effect of the cause, the inner noise effect, and the measurement distortion effect in the observed variables, the post-nonlinear (PNL) causal m…

cs.LG20129 cited

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…

cs.LG20127 cited

Source Separation and Higher-Order Causal Analysis of MEG and EEG

Kun Zhang, Aapo Hyvarinen

Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer…

cs.LG2012109 cited

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…

cs.LG2012351 cited

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…