--- In [email protected], anonymousff <[EMAIL PROTECTED]> wrote: > --- In [email protected], "authfriend" <[EMAIL PROTECTED]> > wrote: > > --- In [email protected], anonymousff <[EMAIL PROTECTED]> > > wrote: > > > --- In [email protected], "authfriend" <[EMAIL PROTECTED]> > > > > But that's exactly what this myth does: it provides > > > > a basis for quantification. From it we can construct > > > > testable hypotheses, e.g., people who live in homes > > > > with south-facing entrances will die at younger ages > > > > than those in homes with entrances facing in other > > > > directions. > > > > > > Yes but... > > > > > > You can do research that shows a correlation between factors, > and > > even > > > gives an indication of which factors may be causal. This is > > important > > > preliminary research. But, when faced with overwhelming > opposition > > to > > > your ideas due to their not fitting with mainstream paradigms, > you > > > need to follow up this research with studies that demonstrate > the > > > actual causal mechanisms for the results being observed. > > > > I'm not sure you can actually *demonstrate* causal > > mechanisms. Rather, you make causal *assumptions* > > to a greater or lesser confidence level, no? > > *** > Yeh, sure. To be technically correct. But this doesn't change the > nature of the research. One kind of research finds correlations, > without looking at what may cause the correlations. Another kind of > research focuses more directly on the processes involved.
On another front, I have been looking at the correlation / causation question. At times I do a lot of multi-variate regression work where say, 10 independent variables clearly "explain" most of the variations in the dependent variable. This can lull one into believing that this demonstrates causality. As a proof it does not, though it may still be a causal relationship and the relationships can be mapped out using influence diagrams. Some interesting links on loosely and formally proving causality are below: http://b-course.hiit.fi/naive_cause.html The scientific research community has adopted rigorous methods to eliminate the need for subjective judgments about many things, but when it comes to testing whether X causes Y, they revert to intuition and hand-waving. This book makes a strong argument that we shouldn't accept that. It demonstrates that it is possible to turn intuitions about causation into hypotheses that are unambiguous and testable. http://www.psych.uni-goettingen.de/abt/1/waldmann/cog_sci00.pdf http://www.amazon.com/exec/obidos/tg/detail/-/0521773628/102-3865279-2055340?v=glance online version of the above: http://bayes.cs.ucla.edu/BOOK-2K/book-toc.html ------------------------ Yahoo! Groups Sponsor --------------------~--> Get fast access to your favorite Yahoo! Groups. Make Yahoo! your home page http://us.click.yahoo.com/dpRU5A/wUILAA/yQLSAA/JjtolB/TM --------------------------------------------------------------------~-> To subscribe, send a message to: [EMAIL PROTECTED] Or go to: http://groups.yahoo.com/group/FairfieldLife/ and click 'Join This Group!' Yahoo! Groups Links <*> To visit your group on the web, go to: http://groups.yahoo.com/group/FairfieldLife/ <*> To unsubscribe from this group, send an email to: [EMAIL PROTECTED] <*> Your use of Yahoo! Groups is subject to: http://docs.yahoo.com/info/terms/
