Causal diagrams for epidemiologic research.

By Sander Greenland, Judea Pearl, James M. Robins3. 1999

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Citations in the corpus

Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “…[cf. Dawid, 2000, Rubin, 2005]; or structural equation modelling [cf. Bollen, 1989, Robins et al., 2000, Pearl, 2009] and corresponding graphical modeling [cf. Lauritzen and Spiegelhalter, 1988, Greenland et al., 1999, Spirtes et al., 2000], where the book by Pearl [2009] provides a nice overview.”
External Validity: From Do-Calculus to Transportability Across Populations (2014, background): “rubrics such as “external validity” (Campbell and Stanley (1963); Manski (2007)), “meta-analysis” (Glass (1976); Hedges and Olkin (1985); Owen (2009)), “heterogeneity” (Höfler et al.”
Handling time varying confounding in observational research (2017, background): “2 12-14 The absence of an arrow pointing from one variable to another indicates that the former does not have a direct causal effect on the latter.”
Causal inference in statistics: An overview (2009, methodology): “Weeding out myths and misconceptions from outdated traditions (Meek and Glymour, 1994; Greenland et al., 1999; Cole and Hernán, 2002; Arah, 2008; Shrier, 2009; Pearl, 2009b).”
Anchor regression: Heterogeneous data meet causality (2018, background): “on causal inference under various types of assumptions and dierent frameworks, with applications ranging from public health to biology and economics [Lauritzen and Spiegelhalter, 1988, Bollen, 1989, Greenland et al., 1999, Spirtes et al., 2000, Robins et al., 2000, Dawid, 2000, Rubin, 2005, Pearl, 2”
Epidemiology of basal-like breast cancer (2008, methodology): “Potential confounders were selected based upon prior knowledge, directed acyclic graphs (DAGs) [26, 27] and by selecting variables that resulted in a 10% or greater change in the beta estimate for the exposures of interest.”
Transportability across studies: A formal approach (2011, background): “The advent of causal diagrams (Pearl, 1995; Greenland et al., 1999; Spirtes et al., 2000; Pearl, 2009) provides such a language and renders the formalization of transportability possible.”
3. The Foundations of Causal Inference (2010, background): “The problem of defining an admissible set, let alone finding one, has baffled epidemiologists and social scientists for decades (see Greenland et al. (1999) and Pearl (1998) for a review).”
For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates (2017, background): “…argued, causal models and diagrams are incredibly useful for avoiding bad judgments, causal fallacies, harmful adjustments, and for identifying sources of bias and valid adjustments (Pearl 1995, 2009; Greenland et al. 1999; Höfler 2005; Phillips and Goodman 2004, 2006; Glymour and Greenland 2008).”
Temperature (2019, unknown): “Taking the mesoscopic point of view of macroscopic systems we have to attempt to combine the two extremes.”
Marginal Structural Models and Causal Inference in Epidemiology (2000, unknown): “pr[AO = O|L0 = lo] = 1/ 1 + E exp(aoj + alo) (10) j=1”