A structural approach to selection bias.¶
Hernán, M. A., Hernández-Díaz, S., & Robins, J. M. (2004). A structural approach to selection bias. Epidemiology, 15(5), 615-625.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Selection Bias
- Selection bias occurs when units' inclusion in the analyzed sample depends on variables that are themselves caused by or associated with both the exposure and outcome, breaking the representativeness of the observed sample relative to the target population
This sourceHernan structural selection bias causal graphs collider conditioning.
Supported in partVerified against the work's full text
The paper gives a structural criterion: the bias arises from conditioning on a common effect of two variables, one the exposure or a cause of it and the other the outcome or a cause of it. Its firefighter example shows that such conditioning “induces selection bias resulting in noncomparability of the exposed and unexposed”, with the restricted sample set against a random sample of the target population. That mechanism and that break in representativeness are what the sentence states. The work's criterion is conditioning on a common effect, so the sentence's broader “associated with” reaches cases the paper classifies as confounding rather than selection bias.
“These examples lead us to propose that the term selection bias in causal inference settings be used to refer to any bias that arises from conditioning on a common effect as in Figure 3 or its variations”
- Selection bias occurs when units' inclusion in the analyzed sample depends on variables that are themselves caused by or associated with both the exposure and outcome, breaking the representativeness of the observed sample relative to the target population
- Selective Propagation
- An affirmative says the path was indifferent to the attribute; a negative says the observed population is a survivor set and has to be handled as one.
This sourceDefines selection structurally, so that a population counts as a survivor set exactly when the probability of appearing in it depends on the attribute under study.
Supported in partVerified against the work's full text
Shows structurally when a restricted study population biases an association (selection depending on exposure/outcome causes) and when it does not, but does not use the affirmative/negative or 'survivor set' framing.
“Under any of these structures, there would be no bias if the study population was a representative (ie, random) sample of the target population. However, bias will be present if the study is restricted to those who volunteered or elected to participate (C = 1).”
- An affirmative says the path was indifferent to the attribute; a negative says the observed population is a survivor set and has to be handled as one.
Domain-specific¶
Verification¶
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Registry ID ref:45cd6839d382 · see in the full table