Applying Causal Inference Methods in Psychiatric Epidemiology

Causal inference is important because it informs etiologic models and prevention efforts. The view that causation can be definitively resolved only with RCTs and that no other method can provide potentially useful inferences is simplistic. Rather, each method has varying strengths and limitations. We need to avoid the extremes of overzealous causal claims and the cynical view that potential causal information is unattainable when RCTs are infeasible. Triangulation, which applies different methods for elucidating causal inferences to address to the same question, may increase confidence in the resulting causal claims.