Roadmap for systematic identification and analysis of multiple biases in causal inference

Observational studies examining causal effects rely on unverifiable assumptions, the violation of which can induce multiple biases. Quantitative bias analysis (QBA) methods examine the sensitivity of findings to such violations, generally, by producing estimates under alternative assumptions, incorporating additional information. Although substantial guidance exists for implementing QBA, there is limited guidance on how to systematically determine the assumptions underlying a primary causal analysis and the potential violations that should guide bias analysis. Consequently, many assumptions remain implicit, leading to selective and therefore misleading QBA. To address this gap, we propose a roadmap for systematically identifying and analysing multiple biases. Briefly, this consists of (i) articulating the assumptions underlying the primary analysis through specification of the "ideal trial" that defines the causal estimand and the assumptions required by its (possibly implicit) emulation, depicting these assumptions using a causal diagram; (ii) extending the diagram to depict alternative assumptions, under which biases may arise; (iii) obtaining a single estimate that simultaneously corrects for all potential biases. We illustrate the roadmap using an investigation of the effect of breastfeeding on risk of childhood asthma, and through simulations demonstrate the need for analysing multiple biases jointly rather than one at a time.