Article

High-dimensional confounding in causal mediation: a comparison study of double machine learning and regularized partial correlation network

2024-10-12

Abstract excerpt

In causal mediation analyses, of interest are the direct or indirect pathways from exposure to an outcome variable. For observation studies, massive baseline characteristics are collected as potential confounders to mitigate selection bias, possibly approaching or exceeding the sample size. Accordingly, flexible machine learning approaches are promising in filtering a subset of relevant confounders, along with est...

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Literature Corpus work: 891f046f-9967-5d3a-bf37-292daaefeafb
DOI: 10.1101/2024.10.12.617110

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