Causal inference with observational data: the need for triangulation of evidence

• Published in: Psychological medicine Vol. 51; no. 4; pp. 563 - 578
• Main Authors: Hammerton, Gemma, Munafò, Marcus R.
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.03.2021
• Subjects: Bias; Causality; Health status; Humans; Inference; Infrastructure; Invited Review; Invited Review Article; Measurement; Mental health; Models, Statistical; Observational studies; Observational Studies as Topic; Population; Research Design; Risk factors; Robustness; Statistical methods; Statistics; Transparency; Triangulation; Variables; epidemiology; triangulation; causal inference; observational data; mental health

The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest....