Determining conditional independence (CI) re-lationships between random variables is a chal-lenging but important task for problems such as Bayesian network learning and causal discovery. We propose a new kernel CI test that uses a sin-gle, learned permutation to convert the CI test problem into an easier two-sample test problem. The learned permutation leaves the joint distri-bution unchanged if and only if the null hypoth-esis of CI holds. Then, a kernel two-sample test, which has been studied extensively in prior work, can be applied to a permuted and an unpermuted sample to test for CI. We demonstrate that the test (1) easily allows the incorporation of prior knowledge during the permutation step, (2) has power competitive with state-of...
Testing for conditional independence is a core aspect of constraint-based causal discovery. Although...
Recently, regression based conditional independence (CI) tests have been employed to solve the probl...
Testing for conditional independence is a core part of constraint-based causal discovery. It is mai...
Determining conditional independence (CI) re-lationships between random variables is a chal-lenging ...
Conditional independence testing is an important problem, especially in Bayesian network learning an...
Conditional independence testing is an im-portant problem, especially in Bayesian net-work learning ...
Proposition. Let T be the set of transformation such that for any P ∈ T and sample y of size n, mean...
Conditional independence (CI) testing is an important tool in causal discovery. Generally, by using ...
We propose a general new method, the conditional permutation test, for testing the conditional indep...
We propose a new conditional dependence measure and a statistical test for conditional independence....
Peer reviewed: TruePublication status: PublishedFunder: Nuffield FoundationFunder: Deutsche Forschun...
The algorithms for causal discovery and more broadly for learning the structure of graphical models ...
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independenc...
The conditional randomization test (CRT) was recently proposed to test whether two random variables ...
It is a common saying that testing for conditional independence, i.e., testing whether whether two r...
Testing for conditional independence is a core aspect of constraint-based causal discovery. Although...
Recently, regression based conditional independence (CI) tests have been employed to solve the probl...
Testing for conditional independence is a core part of constraint-based causal discovery. It is mai...
Determining conditional independence (CI) re-lationships between random variables is a chal-lenging ...
Conditional independence testing is an important problem, especially in Bayesian network learning an...
Conditional independence testing is an im-portant problem, especially in Bayesian net-work learning ...
Proposition. Let T be the set of transformation such that for any P ∈ T and sample y of size n, mean...
Conditional independence (CI) testing is an important tool in causal discovery. Generally, by using ...
We propose a general new method, the conditional permutation test, for testing the conditional indep...
We propose a new conditional dependence measure and a statistical test for conditional independence....
Peer reviewed: TruePublication status: PublishedFunder: Nuffield FoundationFunder: Deutsche Forschun...
The algorithms for causal discovery and more broadly for learning the structure of graphical models ...
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independenc...
The conditional randomization test (CRT) was recently proposed to test whether two random variables ...
It is a common saying that testing for conditional independence, i.e., testing whether whether two r...
Testing for conditional independence is a core aspect of constraint-based causal discovery. Although...
Recently, regression based conditional independence (CI) tests have been employed to solve the probl...
Testing for conditional independence is a core part of constraint-based causal discovery. It is mai...
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