Introduction: We sincerely congratulate Professors Nabi, Bhattacharya, Shpitser, and Robins on their interesting contribution, which addresses challenges in missing data analysis through the framework of potential outcomes. In this paper, the authors explain how missing data problems can be framed as causal inference problems: the complete variables are viewed as counterfactual outcomes, the missingness indicators are treated as treatment variables, and the partially observed variables are interpreted as combinations of potential outcomes and treatments. The authors introduce missing data directed acyclic graphs (m-DAGs), review several missing data models from previous literature that can be represented using m-DAGs, and present identifiability results for various graph structures…