How is causal inference performed in omics studies and what are the limitations?

Causal inference distinguishes causation from correlation in omics data. Approaches: 1) Mendelian Randomization (MR) - uses genetic variants as instrumental variables to test causal effects of exposures on outcomes; SNPs associated with exposure used as instruments; robust to confounding and reverse causation. Methods: inverse variance weighted, MR-Egger (tests pleiotropy), GSMR. 2) Mediation analysis - tests if effect of A on C is mediated through B. 3) Intervention studies - perturbations (CRISPR, drugs) provide causal evidence; Perturb-seq combines single-cell and perturbation. 4) Time-series - Granger causality, dynamic causal modeling. 5) Causal discovery - PC algorithm, FCI for learning causal graphs from observational data. Limitations: genetic instrument validity (pleiotropy, weak instruments); unmeasured confounders in observational data; model assumptions; distinguishing direct from indirect effects; generalizing across populations.