Article

Learning Stable Causal Structures from Perturbed Genomic Data: Robust GRN Inference Under Adversarial Interventions

2026-05-13

Abstract excerpt

Causal discovery from observational data is fundamentally challenged by distribution shifts, which are ubiquitous in biological systems. In gene regulatory networks (GRNs), such shifts often arise from adversarial interventions—either naturally occurring (e.g., pathogenic perturbations, cellular stress) or experimentally engineered (e.g., CRISPR knockout screens). While standard causal discovery methods assume ide...

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Literature Corpus work: b01b57ea-e4a6-5f56-a5fd-20b0ed783672
DOI: 10.14293/pr2199.003581.v1

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