Pearl's D-Separation: One More Step Into Causal Thinking

By L. Hayduk, G. Cummings, Rainer F. Stratkotter, M. Nimmo, Kostyantyn Grygoryev, D. Dosman, Michael W. Gillespie, Hannah Pazderka, Kwame Boadu. 2003

79 citations recorded by the source corpus.

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Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data (2017, background): “Accessible introductions to the underlying concept of d- separation, which determines whether paths transmit association or not, can be found in Hayduk et al. (2003) and, in a very brief form, in Thoemmes’s (2015) Appendix A. 6.”
Reversing Arrows in Mediation Models Does Not Distinguish Plausible Models (2015, unknown): “Copyright # Taylor & Francis Group, LLC ISSN: 0197-3533 print=1532-4834 online DOI: 10.1080/01973533.2015.1049351 scientists, see, for example, Hayduk et al. (2003).”
Graphical Representation of Missing Data Problems (2015, background): “In addition, readers can consult the article by Hayduk et al. (2003) or the chapter by Pearl (2009), entitled “d-separation without tears.””
A General Algorithm for Deciding Transportability of Experimental Results (2013, background): “[66], Glymour and Greenland [67], and Pearl [16, p.”