Imputation approaches for potential outcomes in causal inference
Jessie K. Edwards, Sunni L. Mumford, Daniel J. Westreich, Stephen R. Cole, Robert William Platt, Enrique F. Schisterman · Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2020
Background: The fundamental problem of causal inference is one of missing data, and specifically of missing potential outcomes: if potential outcomes were fully observed, then causal inference could be made trivially. Though often not discussed explicitly in the epidemiological literature, the connections between causal inference and missing data can provide additional intuition.