Clarifying missing at random and related definitions, and implications when coupled with exchangeability

Fabrizia Mealli and Donald B. Rubin

Biometrika, 2015, vol. 102, issue 4, 995-1000

Abstract: We clarify the key concept of missingness at random in incomplete data analysis. We first distinguish between data being missing at random and the missingness mechanism being a missing-at-random one, which we call missing always at random and which is more restrictive. We further discuss how, in general, neither of these conditions is a statement about conditional independence. We then consider the implication of the more restrictive missing-always-at-random assumption when coupled with full unit-exchangeability for the matrix of the variables of interest and the missingness indicators: the conditional distribution of the missingness indicators for any variable that can have a missing value can depend only on variables that are always fully observed. We discuss implications of this for modelling missingness mechanisms.

Date: 2015
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Citations: View citations in EconPapers (11)

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