2023/05/30 by Julian Schuessler, Peter Selb · 1 voice · 10 citations
Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Algorithm #Artificial intelligence #Causal inference #Causal model #Causal structure #Computer science #Data mining #Directed acyclic graph #ENCODE #Econometrics #Graphical model #Inference #Machine learning #Mathematical economics #Mathematics #Outcome (game theory) #Sample size determination #Sampling bias #Selection (genetic algorithm) #Selection bias #Statistical Methods and Bayesian Inference #Statistics #Survey Methodology and Nonresponse #Weighting
paper · pdf · doi:10.1177/00491241231176851
published in Sociological Methods & Research 54(1), 74-105 (SAGE Publishing)
openalex publication_date 2023/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Directed acyclic graphs (DAGs) are now a popular tool to inform causal inferences. We discuss how DAGs can also be used to encode theoretical assumptions about nonprobability samples and survey nonresponse and to determine whether population quantities including conditional distributions and regressions can be identified. We describe sources of bias and assumptions for eliminating it in various selection scenarios. We then introduce and analyze graphical representations of multiple selection stages in the data collection process, and highlight the strong assumptions implicit in using only design weights. Furthermore, we show that the common practice of selecting adjustment variables based on correlations with sample selection and outcome variables of interest is ill-justified and that nonresponse weighting when the interest is in causal inference may come at severe costs. Finally, we identify further areas for survey methodology research that can benefit from advances in causal graph theory.