Causal effect estimation from trans-regulatory single-cell CRISPR screens

Recent advances in single-cell transcriptomics and CRISPR-based genome editing have enabled large-scale perturbation experiments with genome-wide expression readouts. Single-cell CRISPR screens offer the opportunity to move beyond correlation and estimate causal effects of genetic perturbations on gene expression at scale. These approaches promise to substantially deepen insights into cellular functions and disease mechanisms. However, interpreting statistical associations as causal effects requires additional assumptions beyond those needed for standard statistical analyses. In this minireview, we introduce key concepts and principles for causal effect estimation in trans-regulatory single-cell CRISPR studies. We describe a set of assumptions under which estimates from existing statistical methods admit a causal interpretation and provide a concise overview of these approaches. Finally, through an illustrative example, we demonstrate how violations of these assumptions can bias estimated effects.