Lesson plan
Ignorability
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. It has also been called unconfoundedness, selection on the observables, or no omitted variable bias. This idea is part of the Neyman-Rubin Causal Inference Model, developed by Jerzy Neyman in the 1920s, and later extended by Donald Rubin in collaboration with Paul Rosenbaum. The exact definition differs between their articles in that period. In a 1978 article, Rubin discusses ignorable assignment mechanisms, which can be understood as the way individuals are assigned to treatment groups being irrelevant for the data analysis, given everything that is recorded about that individual. In a 1983 paper on propensity score matching, Rubin and Rosenbaum define the stronger condition of a treatment assignment being strongly ignorable, mathematically formulated as (r_1,r_0) \perp \!\!\!\perp z \mid v ,\quad 0 , where r_t is a potential outcome given treatment t , v is some covariates and z is the actual treatment.…
Article summary
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. It has also been called unconfoundedness, selection on the observables, or no omitted variable bias. This idea is part of the Neyman-Rubin Causal Inference Model, developed by Jerzy Neyman in the 1920s, and later extended by Donald Rubin in collaboration with Paul Rosenbaum. The exact definition differs between their articles in that period. In a 1978 article, Rubin discusses ignorable assignment mechanisms, which can be understood as the way individuals are assigned to treatment groups being irrelevant for the data analysis, given everything that is recorded about that individual. In a 1983 paper on propensity score matching, Rubin and Rosenbaum define the stronger condition of a treatment assignment being strongly ignorable, mathematically formulated as (r_1,r_0) \perp \!\!\!\perp z \mid v ,\quad 0 , where r_t is a potential outcome given treatment t , v is some covariates and z is the actual treatment.…
Key claims
No claims recorded.
Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
No sources recorded.
Key claims and bibliography are generated from the same tracked claims and sources as the article page.