Inference and Missing Data.¶
RUBIN, D. B. (1976). Inference and Missing Data. Biometrika, 63(3), 581-592.
Cited by¶
9 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Imputation
- The fill is applied with an explicit assumption about the relationship between observed and missing — missing-completely-at-random, missing-at-random, or missing-not-at-random, in the formal idiom.
This sourceIntroduces the MCAR/MAR/MNAR missingness taxonomy and the conditions under which the missingness mechanism can be ignored — the load-bearing assumption framework for imputation, including the MNAR-blindness result.
- The fill is applied with an explicit assumption about the relationship between observed and missing — missing-completely-at-random, missing-at-random, or missing-not-at-random, in the formal idiom.
- Missing Data Mechanisms (MCAR, MAR, MNAR)
- (1) **Missing data mechanisms classify the process by which observations become missing into three increasingly problematic categories: MCAR (missing completely at random)
This sourceRubin foundational taxonomy of missing-completely-at-random (MCAR).
- where missingness is independent of all variables observed and unobserved; MAR (missing at random)
This sourceRubin missing-at-random (MAR) category in foundational taxonomy.
- where missingness depends only on observed variables; and MNAR (missing not at random)
This sourceRubin missing-not-at-random (MNAR) category in foundational taxonomy.
- … where missingness is independent of all variables observed and unobserved; MAR (missing at random) where missingness depends only on observed variables; and MNAR (missing not at random) where missingness depends on the unobserved values themselves.** (2) The classification, formalized by Donald Rubin in 1976
- Donald Rubin's 1976 paper "Inference and Missing Data" (Biometrika)
This sourceRubin foundational missing-data taxonomy paper.
- (1) **Missing data mechanisms classify the process by which observations become missing into three increasingly problematic categories: MCAR (missing completely at random)
- Selection Bias
- The 1936 Literary Digest failure (2.4 million respondents predicting the wrong election outcome) is the classic demonstration
This sourceRubin foundational taxonomy of missing-completely-at-random (MCAR).
- The 1936 Literary Digest failure (2.4 million respondents predicting the wrong election outcome) is the classic demonstration
- Selection-Visibility Gate
- Dropout uncorrelated with expression is attrition, not a gate.
This sourceSeparates missingness independent of the underlying value from missingness that depends on it, which is what distinguishes ordinary attrition from a selective gate.
- Dropout uncorrelated with expression is attrition, not a gate.
- Silence as Signal
- The cost-asymmetric mechanism is precisely what makes the pattern substrate-independent, operating in physics through detection thresholds, in biology through sampling effort, in social systems through reporting cost, and in digital systems through logging asymmetries; a practitioner who has corrected for it once carries both the diagnostic and the four-family repair into every record that was expensive to produce and cheap to omit.
This sourceFoundational taxonomy of missing-data mechanisms (MCAR, MAR, MNAR); silence-as-signal is the missing-not-at-random case with a cost-asymmetric mechanism.
- The cost-asymmetric mechanism is precisely what makes the pattern substrate-independent, operating in physics through detection thresholds, in biology through sampling effort, in social systems through reporting cost, and in digital systems through logging asymmetries; a practitioner who has corrected for it once carries both the diagnostic and the four-family repair into every record that was expensive to produce and cheap to omit.
Verification¶
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