Causal diagrams for epidemiologic research.¶
Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1), 37-48.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Collider (Causal Graph)
- Lord's Paradox
- … (of which Lord's is the continuous-variable, pre-post analogue), the collider-bias and selection-on-the-dependent-variable patterns, `selection_bias` (when baseline differences reflect non-random assignment), and the `causal_inference` programme's standing principle that statistical adjustment is causally non-neutral
This sourceCausal diagrams identify the variables that must be controlled to obtain unconfounded effect estimates, so adjustment is not a neutral operation.
Supported in partVerified against the publisher's abstract
“Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates.”
- … (of which Lord's is the continuous-variable, pre-post analogue), the collider-bias and selection-on-the-dependent-variable patterns, `selection_bias` (when baseline differences reflect non-random assignment), and the `causal_inference` programme's standing principle that statistical adjustment is causally non-neutral
Verification¶
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