By Shipley B.
Invoice Shipley explores the logical and methodological relationships among correlation and causation. He provides a sequence of statistical equipment that may attempt, and probably realize, cause-effect relationships among variables in occasions the place it isn't attainable to behavior randomized, or experimentally managed, experiences. a lot of those equipment are relatively new and so much are often unknown to biologists. in addition to describing how one can behavior those statistical assessments, he additionally places the tools into old context and explains after they can and can't justifiably be used to check causal claims. Hb ISBN (2000); 0-521-79153-7
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Additional info for Cause and correlation in biology
5. Conditioning on a vertex in a causal graph means to 28 2 . 4 d - S E PA R AT I O N change its state; if it was active, then conditioning inactivates it but, if it was inactive, then conditioning activates it. So, since vertex C along the path A→C→D is naturally active (ON), conditioning on it changes its state to inactive (OFF), thus blocking any indirect causal inﬂuence of A on D. 4 d-separation Remembering that we are still not discussing probability distributions or statistical models, and are still concerned only with properties of directed acyclic graphs, we can now deﬁne what is meant by ‘independence’ of vertices, or of groups of vertices, in a causal graph upon conditioning on some other set of vertices.
I once came close to being beaten by an enraged boyfriend simply because (I thought) I was complimenting his girlfriend on her long hair, which was drawn in a ponytail. The word for ‘tail’ in French is queue, which takes a feminine gender. There is another word in colloquial Canadian French, cul (the ‘l’ is silent), that sounds almost the same. It takes a masculine gender, is pronounced only slightly diﬀerently, and can be roughly translated as a person’s rear end; the correctly translated word rhymes with ‘pass’ but the reader will understand if I don’t give the literal translation.
Second, use the translation device (d-separation) to translate from this directed graph into the well-known mathematical language (probability theory) that is used in statistics to express notions of association. Finally, determine the types of (conditional) independence relationship that must occur in the resulting joint probability distribution. Continuing with the analogy of a correlation as being an observational shadow of the underlying causal process, the translation device (d-separation) is the method by which one can predict these shadows.
Cause and correlation in biology by Shipley B.