Beyond Perturbation Prediction: Learning Directed Gene Structure with RISCAL

Predicting genetic perturbation responses and recovering their regulatory mechanisms are central to understanding gene function and therapeutic target prioritization. Existing methods often emphasize response prediction or structure recovery, while assuming perfect perturbations or modeling imperfect ones as unrestricted mechanism changes. We introduce RISCAL (Residual-activity Interventions for Structural Causal Learning), a nonlinear gene-level structural causal model that jointly learns directed dependencies and predicts responses under residual-activity interventions. Its intervention model captures imperfect loss-of-function by separating gene-specific residual functional activity from measured transcript abundance. RISCAL accurately recovers synthetic ground-truth DAGs and matches the strongest predictive baselines on LINCS L1000 profiles from ten cell lines. Its predicted graph cores are enriched for CollecTRI/SIGNOR interactions and form coherent, partially conserved functional programs. RISCAL can also incorporate unseen regulators and predict responses consistent with experimental evidence.