Identifiability, Exchangeability, and Epidemiological Confounding¶
GREENLAND, S., & ROBINS, J. M. (1986). Identifiability, Exchangeability, and Epidemiological Confounding. International Journal of Epidemiology, 15(3), 413-419.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Confounding
- The common-cause third variable
This sourceDraws the formal connection between identifiability, exchangeability, and confounding; supports the common-cause/exchangeability framing (D25-016, D25-021, D25-027).
Borderline supportVerified against the publisher's abstract
Abstract ties confounding to non-exchangeability of exposure groups, not to the explicit common-cause third-variable formulation the marker names.
“confounding: bias in estimation of the effects of an exposure on disease risk, due to inherent differences in risk between exposed and unexposed individuals ... reveals the exchangeability assumptions that are implicit in confounder control methods”
Two runs of the same check disagreed on this citation; the later verdict is shown and the citation is queued for review.
- The common-cause third variable
- Randomization
- 3. The confounding-control via balanced covariate distribution — by severing the association between treatment assignment and all pre-treatment confounders through the random mechanism itself, randomization eliminates confounding bias without requiring measurement or statistical adjustment for all confounders
This sourceGreenland-Robins formal causal-inference framework exchangeability back-door criterion.
- 3. The confounding-control via balanced covariate distribution — by severing the association between treatment assignment and all pre-treatment confounders through the random mechanism itself, randomization eliminates confounding bias without requiring measurement or statistical adjustment for all confounders
- Selection Bias
- Sampling representativeness (#433) is compromised by selection bias, making the two primes tightly coordinated. Not eliminated by randomization — randomization addresses selection-into-exposure (who receives treatment) but not selection-into-study (who is observed at all)
This sourceGreenland-Robins formal causal-inference framework exchangeability back-door criterion.
- Sampling representativeness (#433) is compromised by selection bias, making the two primes tightly coordinated. Not eliminated by randomization — randomization addresses selection-into-exposure (who receives treatment) but not selection-into-study (who is observed at all)
Verification¶
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Does it back the claim? Read against the text for 1 of 3 citations: 1 borderline. Each verdict is shown under its citation below, with what in the work backs the sentence.
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Registry ID ref:dfd0eac91b9e · see in the full table