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In statistics, ignorability refers to an experiment design where the method of data collection (and the nature of missing data) do not depend on the missing data. A missing data mechanism such as a treatment assignment or survey sampling strategy is "ignorable" if the missing data matrix, which indicates which variables are observed or missing, is independent of the missing data conditional on the observed data. This idea is part of the Rubin Causal Inference Model, developed by Donald Rubin in collaboration with Paul Rosenbaum in the early 1970s. Pearl () devised a simple graphical criterion, called ''back-door'', that entails ignorability and identifies sets of covariates that achieve this condition. == External links == * (Ignorability in Statistical and Probabilistic Inference ) by Manfred Jaeger 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Ignorability」の詳細全文を読む スポンサード リンク
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