Introduction: This paper presents a new perspective on missing data by integrating causal and counterfactual frameworks, reinterpreting missing data as a causal inference problem. By drawing an analogy between missing data and unobserved counterfactuals, the paper advances a structured approach to understanding and identifying parameters under different missingness mechanisms, particularly in Missing Not At Random (MNAR) settings. Utilizing Directed Acyclic Graphs (DAGs) and their extensions to missing data DAGs (m-DAGs), this work provides a rigorous framework for characterizing dependencies and assumptions in missing data problems. A key contribution is the extension of the g-formula to accommodate counterfactual distributions in missing data models, offering new insights and methodological advancements for addressing MNAR challenges…