Moving from Association to Causation: Instrumental factor models for causal inference in high-dimensional multi-omics data
Distinguishing true causal relationships from mere association is a core challenge in bioinformatics and science in general. Causal inference, in particular in "-omics" studies, is hindered by high dimensionality, correlated measurements, and pervasive endogeneity arising from unmeasured confounding and complex interventions. We introduce Factor IV, a supervised instrumental variable (IV) framework for identifying causal effects in high-dimensional biological systems. FactorIV constructs low-dimensional instrumental factors via a sparse low-rank decomposition of the instrument-exposure map, yielding an identifiable first stage even when either/both endogenous and instrumental variables are high-dimensional. Under standard IV assumptions, these factors remain orthogonal to outcome noise while capturing coordinated, perturbation-driven biological variation. The framework supports generalized first-stage models, including Gaussian, Bernoulli and negative binomial likelihoods. Simulation studies demonstrate accurate recovery of factor-level and feature-level causal effects under linear and generalized settings, with robustness to correlated errors and hidden confounding. To illustrate biological discovery, we applied FactorIV to a mouse hepatocellular carcinoma model and the DIABIMMUNE infant cohort. We demonstrate how FactorIV uncovers mechanistically interpretable causal structure beyond association-based analyses.