In statistics

Statistics

Statistics is the study of the collection, organization, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments....

, 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 the on the observed data.

This idea is part of the Rubin Causal Inference Model

Rubin Causal Model

The Rubin Causal Model is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes. RCM is named after Donald Rubin, Professor of Statistics at Harvard University...

, developed by Donald Rubin

Donald Rubin

Donald Bruce Rubin is the John L. Loeb Professor of Statistics at Harvard University. He was hired by Harvard in 1984, and served as chair of the department from 1985-1994....

 in collaboration with Paul Rosenbaum in the early 1970s.

Pearl [2000] 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