首页|High-dimensional confounding in causal mediation: a comparison study of double m achine learning and regularized partial correlation network
High-dimensional confounding in causal mediation: a comparison study of double m achine learning and regularized partial correlation network
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: "In causal mediation analyses, of interest are the direct or indirect pathways f rom exposure to an outcome variable. For observation studies, massive baseline c haracteristics are collected as potential confounders to mitigate selection bias , possibly approaching or exceeding the sample size. Accordingly, flexible machi ne learning approaches are promising in filtering a subset of relevant confounde rs, along with estimation using the efficient influence function to avoid overfi tting. Among various confounding selection strategies, two attract growing atten tion. One is the popular debiased, or double machine learning (DML), and another is the penalized partial correlation via fitting a Gaussian graphical network m odel between the confounders and the response variable.
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