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20092023
most citedKernel-based Conditional Independence Test and Application in Causal Discovery

351 citations · 1.8k across the 37 of their papers we have counts for

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Showing 2012 · cs.LGShow all

5 papers · 2 filters

cs.LG2012★ 12 cited

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…

cs.LG2012★ 127 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…

cs.LG2012★ 351 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…

cs.LG2012★ 64 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…

cs.LG2012★ 13 cited

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…