Causal Inference Tools for Pharmacovigilance: Using Causal Graphs to Identify and Address Biases in Disproportionality Analysis

Using DAGs to map and mitigate biases requires caution and does not allow to obtain definitive answers to causal questions. Still, it results in more reliable and knowledge-based safety signals, reducing and mapping the gap between what we find (association) and what we look for (causation). Additional research should further tailor DAGs to pharmacovigilance challenges, map the generative mechanisms of pharmacovigilance data, and better integrate disproportionality analysis results into evidence-synthesis workflows.