An overview of relations among causal modelling methods.

By Sander Greenland, Babette A. Brumback. International Journal of Epidemiology, 2002

409 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/849d96b09cd7be4aacf7eb1dc283a056e377a478

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Citations in the corpus

Pathways between education and health: a causal modelling approach (2006, background): “SEMs can be used to specify ‘causal models’ through which causal hypotheses about the relationships between variables can be constructed and tested (Pearl, 1995; Greenland, 2000; Sobel, 2000; Greenland and Brumback, 2002).”
Behavioral science at the crossroads in public health: extending horizons, envisioning the future. (2006, background): “While several models of disease causation have risen and fallen in the history of public health research (see for example Greenland & Brumback, 2002), causal reasoning has generally been based, in whole or in part, on a variant of the counterfactual model (or potential outcome model) (Little &…”
For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates (2017, background): “Contrary to some critics, causal diagrams do not require counterfactuals or any particular form or interpretation for the included variables (Spirtes et al. 2001; Greenland and Brumback 2002; Robins and Richardson 2011).”
Minimal sufficient causation and directed acyclic graphs (2009, background): “BY TYLER J. VANDERWEELE AND JAMES M. ROBINS”
Intellectual Impairment in School-Age Children Exposed to Manganese from Drinking Water (2010, methodology): “The first set of covariates (model A) was chosen based on the examination of directed acyclic graphs (Greenland and Brumback 2002) and included several socioeconomic indicators.”
How to control confounding effects by statistical analysis (2012, background): “A successful randomization minimizes confounding by unmeasured as well as measured factors, whereas statistical control that addresses confounding by measurement and can introduce confounding through inappropriate control (7-9).”
Experimental designs for identifying causal mechanisms (2013, background): “, Greenland and Brumback, 2002; Rothman and Greenland, 2005; VanderWeele and Robins, 2007, 2008, 2009). VanderWeele (2009) formally studies the relationship of this alternative definition to the process-based definition of the causal mechanism using a diagrammatic approach.”
Empirical and counterfactual conditions for sufficient cause interactions (2008, background): “Rothman & Greenland (1998), Greenland & Brumback (2002) and Flanders (2006) provide some further discussion.”