351 citations · 1.8k across the 37 of their papers we have counts for
5 papers · 1 filter
Invariant Models for Causal Transfer Learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner +1
Methods of transfer learning try to combine knowledge from several related tasks (or domains) to improve performance on a test task. Inspired by causal methodology, we relax the us…
backShift: Learning causal cyclic graphs from unknown shift interventions
Dominik Rothenhäusler, Christina Heinze, Jonas Peters +1
We propose a simple method to learn linear causal cyclic models in the presence of latent variables. The method relies on equilibrium data of the model recorded under a specific ki…
Removing systematic errors for exoplanet search via latent causes
Bernhard Schölkopf, David W. Hogg, Dun Wang +4
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired b…
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu +1
We describe a method to perform functional operations on probability distributions of random variables. The method uses reproducing kernel Hilbert space representations of probabil…
Causal inference using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, Nicolai Meinshausen
What is the difference of a prediction that is made with a causal model and a non-causal model? Suppose we intervene on the predictor variables or change the whole environment. The…