Causal Diagrams

By Sander Greenland, Judea Pearl. 2011

375 citations recorded by the source corpus.

Subfields: Artificial Intelligence, History and Philosophy of Science

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Citations in the corpus

Causal inference in statistics: An overview (2009, methodology): “…the basic elements of SCM were introduced in the mid 1990’s (Pearl, 1995a), and have been adapted widely by epidemiologists (Greenland et al., 1999; Glymour and Greenland, 2008), statisticians (Cox and Wermuth, 2004; Lauritzen, 2001), and social scientists (Morgan and Winship, 2007), its…”
TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS (2014, positive): “It is for these reasons that graphical representations have become an indispensable second language in the health sciences (Glymour and Greenland, 2008; Vansteelandt and Lange, 2012) and are making their way towards the social and behavioral sciences (Morgan and Winship, 2007; Chalak and White,…”
Recovering from Selection Bias in Causal and Statistical Inference (2014, background): “, Genetics (Pirinen, Donnelly, and Spencer 2012; Mefford and Witte 2012), Economics (Heckman 1979; Angrist 1997), and Epidemiology (Robins 2001; Glymour and Greenland 2008)).”
Causal Inference in Accounting Research (2016, methodology): “Finally in Figure 1c, we have Z acting as what is referred to as a “collider” variable (Glymour and Greenland, 2008; Pearl, 2009a).”
Controlling Selection Bias in Causal Inference (2011, background): “, cases may be reported only when the outcome (disease or complication) is unusual, while non-cases remain unreported (see (Glymour and Greenland, 2008; Robins et al., 2000; Robins, 2001; Hernán et al., 2004)).”
Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies (2010, neutral): “Hill himself, as well as more recent authors,16,20,21 have been ambivalent about this particular guideline.”
Causality matters in medical imaging (2019, background): “In addition, the granularity of ‘direct effects’ is only relative to the chosen level of abstraction 66 .”
The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. (2013, background): “This sort of effect variation (variation without a specified source) has been termed “effect heterogeneity” or “effect measure modification” (5, 13, 15), although the term “effect measure modification” (or worse, “effect modification”) is problematic because it evokes the more narrow concept of caus”
Action and Perception as Divergence Minimization (2020, background): “agent selects the values of these variables, we have to condition the objective terms on them as causal interventions (Pearl, 1995; Ortega and Braun, 2010).”
Causal inference and the data-fusion problem (2016, background): “Selection bias challenges the validity of inferences in several tasks in artificial intelligence (27, 28) and statistics (29, 30) as well as in the empirical sciences [e.g., genetics (31, 32), economics (33, 34), and epidemiology (35, 36)].”
Generalized Adjustment Under Confounding and Selection Biases (2018, background): “, genetics (Pirinen, Donnelly, and Spencer 2012; Mefford and Witte 2012), economics (Heckman 1979; Angrist 1997), and epidemiology (Robins 2001; Glymour and Greenland 2008)).”
Recovering Causal Effects from Selection Bias (2015, background): “, genetics (Pirinen, Donnelly, and Spencer 2012; Mefford and Witte 2012), economics (Heckman 1979; Angrist 1997), and epidemiology (Robins 2001; Glymour and Greenland 2008)).”