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DIET: Conditional independence testing with marginal dependence measures of residual information. (arXiv:2208.08579v1 [stat.ME])
Aug. 19, 2022, 1:11 a.m. | Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath
stat.ML updates on arXiv.org arxiv.org
Conditional randomization tests (CRTs) assess whether a variable $x$ is
predictive of another variable $y$, having observed covariates $z$. CRTs
require fitting a large number of predictive models, which is often
computationally intractable. Existing solutions to reduce the cost of CRTs
typically split the dataset into a train and test portion, or rely on
heuristics for interactions, both of which lead to a loss in power. We propose
the decoupled independence test (DIET), an algorithm that avoids both of these …
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