In statistics, ignorability is a feature of an experiment design whereby the method of data collection (and the nature of missing data) does 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. The exact definition differs between their articles in that period. In one of Rubins articles from 1978 Rubin discuss ignorable assignment mechanisms, which can be understood as the way individuals are assigned to treatment groups is irrelevant for the data analysis, given everything that is recorded about that individual. Later, in 1983 Rubin and Rosenbaum rather define strongly ignorable treatment assignment which is a stronger condition, mathematically formulated as , where is a potential outcome given treatment , is some covariates and is the actual treatment. Pearl devised a simple graphical criterion, called back-door, that entails ignorability and identifies sets of covariates that achieve this condition. Ignorability means we can ignore how one ended up in one vs. the other group (‘treated’ , or ‘control’ ) when it comes to the potential outcome (say ). It has also been called unconfoundedness, selection on the observables, or no omitted variable bias. Formally it has been written as , or in words the potential outcome of person had they been treated or not does not depend on whether they have really been (observable) treated or not. We can ignore in other words how people ended up in one vs. the other condition, and treat their potential outcomes as exchangeable. While this seems thick, it becomes clear if we add subscripts for the ‘realized’ and superscripts for the ‘ideal’ (potential) worlds (notation suggested by David Freedman. So: Y11/*Y01 are potential Y outcomes had the person been treated (superscript 1), when in reality they have actually been (Y11, subscript 1), or not (*Y01: the signals this quantity can never be realized or observed, or is fully contrary-to-fact or counterfactual, CF). Similarly, are potential outcomes had the person not been treated (superscript ), when in reality they have been , subscript or not actually (. Only one of each potential outcome (PO) can be