Causality: a Statistical View

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Effective connectivity: Influence, causality and biophysical modeling (2011, unknown): “058 Despite philosophical disagreements about the study of causality, there seems to be a consensus that causal modeling is a legitimate statistical enterprise (Cox and Wermuth, 2004; Frosini, 2006; Pearl, 2003).”
Causal inference in statistics: An overview (2009, background): “…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 potentials as a comprehensive theory of causation are…”
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings (2014, background): “ward, but we were unable to nd it in the literature. There is a somewhat similar recursive formula for regression coecients [7], 1pj2 = p 12 2pj1, which is considered in the causality context (e.g., [11; 10]). However, the main dierence is that the expression for (1;2) 1p in (5) contains causal eect”
What can Statistics Contribute to a Causal Understanding? (2007, background): “Cox & Wermuth (2004) gives an interesting distinction between different levels of causality.”