Using causal diagrams to guide analysis in missing data problems
Using causal diagrams to guide analysis in missing data problems
Estimating causal effects from incomplete data requires additional and inherently untestable assumptions regarding the mechanism giving rise to the missing data. We show that using causal diagrams to represent these additional assumptions both complements and clarifies some of the central issues in missing data theory, such as Rubin's classification of …