Article

Comparison of nuisance function construction strategies for double machine learning causal inference in single-cell transcriptomics: shared unsupervised deep learning does not require cross-fitting

2026-07-26

Abstract excerpt

Inferring “whether a change in the expression of a given gene causally affects the disease state” from observational single-cell transcriptomic data is one of the central problems in single-cell biology. The difficulty lies in confounding: cell state, batch, cell cycle, and the co-expression of other genes may all simultaneously influence the target gene (treatment variable T) and the disease label (outcome variab...

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Literature Corpus work: 5e21119b-2178-5271-9bf7-d7e7f94e3abd
DOI: 10.64898/2026.07.22.739971

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