<div dir="auto">That tutorial is mostly a user's manual for the software.<div dir="auto"><br></div><div dir="auto">A better explanation is on pp. 19 and following in </div><div dir="auto"><br></div><div dir="auto"><a href="https://www.researchgate.net/publication/242448131_Causation_Prediction_and_Search">https://www.researchgate.net/publication/242448131_Causation_Prediction_and_Search</a></div><div dir="auto"><br></div><div dir="auto">Sorry, Glen, it's a book but you only need a few pages to see how correlation (in the form of conditional independence) is causation.</div><div dir="auto"><br></div><div dir="auto">Frank</div><div dir="auto"><br><br><div data-smartmail="gmail_signature" dir="auto">---<br>Frank C. Wimberly<br>140 Calle Ojo Feliz, <br>Santa Fe, NM 87505<br><br>505 670-9918<br>Santa Fe, NM</div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Jul 9, 2020, 11:59 AM Frank Wimberly <<a href="mailto:wimberly3@gmail.com">wimberly3@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="auto">Here is a TETRAD tutorial. See the Calvin and Hobbes cartoon.<div dir="auto"><br></div><div dir="auto"><br><a href="https://rawgit.com/cmu-phil/tetrad/development/tetrad-gui/src/main/resources/resources/javahelp/manual/tetrad_tutorial.html" target="_blank" rel="noreferrer">https://rawgit.com/cmu-phil/tetrad/development/tetrad-gui/src/main/resources/resources/javahelp/manual/tetrad_tutorial.html</a><br><div data-smartmail="gmail_signature" dir="auto">---<br>Frank C. Wimberly<br>140 Calle Ojo Feliz, <br>Santa Fe, NM 87505<br><br>505 670-9918<br>Santa Fe, NM</div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Jul 9, 2020, 11:55 AM Frank Wimberly <<a href="mailto:wimberly3@gmail.com" target="_blank" rel="noreferrer">wimberly3@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="auto">Thanks, Jon. I gave a Friam Colloquium at SFI on the topic back in the day. Unless you have background knowledge, which you usually do, you can't decide which of two correlated variables causes the other. But if you have a set of variables as you usually do in social science and medicine you can use the Causal Markov Condition and the concept of Faithfulness to infer an equivalence class of causal digraphs which will frequently share a set of causal edges.<div dir="auto"><br></div><div dir="auto">I will look for a good tutorial on the subject and mail a link to Friam.<br><br><div data-smartmail="gmail_signature" dir="auto">---<br>Frank C. Wimberly<br>140 Calle Ojo Feliz, <br>Santa Fe, NM 87505<br><br>505 670-9918<br>Santa Fe, NM</div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Jul 9, 2020, 11:34 AM Jon Zingale <<a href="mailto:jonzingale@gmail.com" rel="noreferrer noreferrer" target="_blank">jonzingale@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Frank,<br>
<br>
Would you say more about the work? What sort of framework is used to speak<br>
about the relation between correlation and causation?<br>
<br>
<br>
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