Ignoring Non-ignorable Missingness
Sophia Rabe‐Hesketh, Anders Skrondal
Abstract
The classical missing at random (MAR) assumption, as defined by Rubin (Biometrika 63:581-592, 1976), is often not required for valid inference ignoring the missingness process. Neither are other assumptions sometimes believed to be necessary that result from misunderstandings of MAR. We discuss three strategies that allow us to use standard estimators (i.e., ignore missingness) in cases where missingness is usually considered to be non-ignorable: (1) conditioning on variables, (2) discarding more data, and (3) being protective of parameters.
Topics & Concepts
Missing dataEstimatorInferenceStatisticsEconometricsStatistical inferenceMathematicsComputer scienceArtificial intelligenceStatistical Methods and Bayesian InferenceStatistical Methods and InferenceBayesian Modeling and Causal Inference