Causal differential expression analysis under unmeasured confounders with causarray.

Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cell resolution. However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured confounders, particularly in heterogeneous datasets. To address these challenges, we introduce causarray, a doubly robust causal inference framework for analyzing array-based genomic data at both bulk-cell and single-cell levels.