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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.
Package details |
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Author | Andreas Rieckmann [aut, cre], Piotr Dworzynski [aut], Leila Arras [ctb], Claus Thorn Ekstrom [aut] |
Maintainer | Andreas Rieckmann <aric@sund.ku.dk> |
License | GPL-2 |
Version | 1.1.2 |
URL | https://bioconductor.org |
Package repository | View on CRAN |
Installation |
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