Graphical Causal Models for Survey Inference

Directed acyclic graphs (DAGs) are an increasingly popular tool to inform causal inferences in observational research. We demonstrate how DAGs can be used to encode and communicate theoretical assumptions about nonprobability samples and survey nonresponse, determine whether typical population parameters of interest to survey researchers can be identified from a sample, and support the choice of adjustment strategies. Following an introduction to basic concepts in graph and probability theory, we discuss sources of bias and assumptions for eliminating it in selection scenarios familiar from the missing data literature. We then introduce and analyze graphical representations of the multiple selection stages in the survey data collection process, in line with the Total Survey Error approach. Finally, we identify areas for future survey methodology research that can benefit from advances in causal graph theory.