CoOL: Causes of Outcome Learning

Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <doi:10.1093/ije/dyac078>. The optional 'ggtree' package can be obtained through Bioconductor.

Getting started

Package details

AuthorAndreas Rieckmann [aut, cre], Piotr Dworzynski [aut], Leila Arras [ctb], Claus Thorn Ekstrom [aut]
MaintainerAndreas Rieckmann <>
Package repositoryView on CRAN
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CoOL documentation built on May 24, 2022, 5:04 p.m.