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On Mon, Apr 22, 2013 at 1:15 PM, Lorenzo Isella <lorenzo.ise...@gmail.com> wrote: > On Mon, 22 Apr 2013 17:27:05 +0200, Robert Baer <rb...@atsu.edu> wrote: > > >> It strikes me that this is not a particularly productive approach to >> causality, particularly in an observational setting. You would need to >> design an experiment where you had a known manipulation of an explanatory >> variable and studied the change in a response variable, and then, you came >> back with the roles reversed. I don't think R or indeed any statistical >> package can help you here. >> > > I certainly agree, but here we are not planning an experiment: we have some > data sets that were collected having something totally different in mind and > we wonder if we can extract any info about causality from them. As others have said: (Short answer) No. (Long Answer) Don't be ridiculous. -- Bert > Cheers > > Lorenzo > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
