4 ms·
Good reading in addition to Pearl himself is via Cosma Shalizi. Ref list: http://vserver1.cscs.lsa.umich.edu/~crshalizi/notebooks/causality.html http://vserve
by kevinalexbrown 13y ago
Good reading in addition to Pearl himself is via Cosma Shalizi.
Ref list:
http://vserver1.cscs.lsa.umich.edu/~crshalizi/notebooks/causality.html http://vserver1.cscs.lsa.umich.edu/~crshalizi/notebooks/caus...
Chapters:
http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch22.pdf http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch22.pdf
http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch23.pdf http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch23.pdf
http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch24.pdf http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch24.pdf
Incidentally, Shalizi is a great source for going back to the basics. His course at CMU "Advanced Data Analysis from an Elementary Point of View" has course notes and exercises available gratis: http://www.stat.cmu.edu/~cshalizi/uADA/13/ http://www.stat.cmu.edu/~cshalizi/uADA/13/. And any time I need to get a reading list, the best place to start is usually his 'notebooks' page.
- saint-loup 13y agoI concur. His notebook is a wonder of great resources on many (many many) subjects, with often interesting comments. http://vserver1.cscs.lsa.umich.edu/~crshalizi/notabene/ http://vserver1.cscs.lsa.umich.edu/~crshalizi/notabene/
- Rickasaurus 13y agoAlso, if you're interested in doing this computationally, you should check out Probabilistic Graphical Models. There's a great Coursera course.