most citedOn Causal and Anticausal Learning

127 citations · 374 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI201433 cited

From Ordinary Differential Equations to Structural Causal Models: the deterministic case

Joris Mooij, Dominik Janzing, Bernhard Schoelkopf

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal…

cs.LG2012127 cited

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…

stat.ML201241 cited

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…

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.LG201264 cited

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…