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Conceptual, computational and inferential benefits of the missing data perspective in applied and theoretical statistical problems
Authors:Donald B. Rubin
Affiliation:(1) Department of Statistics, Harvard University, Cambridge, MA, USA
Abstract:This article advocates the following perspective: When confronting a scientific problem, the field of statistics enters by viewing the problem as one where the scientific answer could be calculated if some missing data, hypothetical or real, were available. Thus, statistical effort should be devoted to three steps:
1.  formulate the missing data that would allow this calculation,
2.  stochastically fill in these missing data, and
3.  do the calculations as if the filled-in data were available.
This presentation discusses: conceptual benefits, such as for causal inference using potential outcomes; computational benefits, such as afforded by using the EM algorithm and related data augmentation methods based on MCMC; and inferential benefits, such as valid interval estimation and assessment of assumptions based on multiple imputation. JEL classification  C10, C14, C15
Keywords:Causal inference  EM and extension  multiple imputation  Data Augmentation and extensions  MCMC  posterior predictive model comparisons
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