Constructing causal diagrams for common perinatal outcomes: benefits limitations and motivating examples with maternal antidepressant use in pregnancy

摘要

BackgroundCovariate selection to reduce bias in observational data analysis has primarily relied upon statistical criteria to guide researchers. This approach may lead researchers to condition on variables that ultimately increase bias in the effect estimates. The use of directed acyclic graphs (DAGs) aids researchers in constructing thoughtful models based on hypothesized biologic mechanisms to produce the least biased effect estimates possible.