351 citations · 1.8k across the 37 of their papers we have counts for
8 papers · 1 filter
Counterfactual Reasoning and Learning Systems
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela +6
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes t…
Causal Inference on Time Series using Structural Equation Models
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal inference uses observations to infer the causal structure of the data generating system. We study a class of functional models that we call Time Series Models with Independe…
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…
Identifying confounders using additive noise models
Dominik Janzing, Jonas Peters, Joris Mooij +1
We propose a method for inferring the existence of a latent common cause ('confounder') of two observed random variables. The method assumes that the two effects of the confounder…
Identifiability of Gaussian structural equation models with equal error variances
Jonas Peters, Peter Bühlmann
We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding…
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…