Identifying Complete Data Distributions from Partially Observed Data Using Causal and Counterfactual Reasoning

The core message of this article is that missing data problems can be viewed as a form of causal inference, where the goal is to identify the complete data distribution from the observed data distribution by leveraging graphical representations and counterfactual reasoning.