Causal diagrams for epidemiologic research.

By Sander Greenland, Judea Pearl, James M. Robins. 1999

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Citations in the corpus

Improving the accuracy of medical diagnosis with causal machine learning (2020, background): “Emphysema is positively correlated with the patient ’ s symptoms, but this is primarily due to common causes 52 .”
Uniform consistency in causal inference (2003, background): “Furthermore, conditioning on inappropriate variables can introduce bias into the procedures; see Spirtes et al. (1998) and Greenland et al. (1999).”
The Bradford Hill considerations on causality: a counterfactual perspective (2005, unknown): “This possibility has, for instance, been discussed for the role of anxiety in the development of depression [ 31 ].”
Statistical Models for Causation (2006, background): “Leamer (1978) discusses the issues created by specification searches, as does Hendry (1993). Heckman (2000) traces the development of econometric thought from Haavelmo and Frisch onwards, stressing the role of “structural” or “invariant” parameters, and “potential outcomes.”
Gformula: Estimating Causal Effects in the Presence of Time-Varying Confounding or Mediation using the G-Computation Formula (2011, unknown): “Furthermore, since U and A0 both influence L1, conditioning on L1 induces a non-causal association between A0 and U (see Greenland et al., 1999), thereby opening up a new backdoor path from A0 through U to Y .”
Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness (2015, background): “DAGs in which conditioning on a variable leads to connecting its parents [23, 39].”