The new edition will (1) provide technical corrections, updates, and clarifications to all ten chapters in the original book; (2) add summaries of new developments at the end of each chapter; (3) elucidate subtle issues that readers have found perplexing in a new chapter.
Information about the upcoming release, including an updated table of contents, may be found here: http://bayes.cs.ucla.edu/BOOK-09/causality2-excerpts.htm.
We welcome your comments.
For those who may be interested in learning about current mathematical techniques applied to brain images for measuring, mapping, and modeling brain structure and function, the Institute of Pure & Applied Mathematics at UCLA will be holding a two-week workshop July 14-25. Topics will include Bayesian methods in fMRI, random field methods, multivariate methods, and connectivity models.
Description
This two-week intensive workshop will focus on mathematical techniques applied to brain images to measure, map and model brain structure and function. Topics will range from modeling anatomical structures in MRI scans, and mapping connectivity in diffusion tensor images, to statistical analysis of functional brain images from fMRI and other imaging modalities. Current applications in radiology and neuroscience will be highlighted, as will new directions in the mathematics of structural and functional image analysis. In the second week on Functional Brain Mapping, a series of lectures on diffusion tensor imaging will discuss mathematics and tools for registration, segmentation, fiber tracking and connectivity modeling in tensor and “beyond-tensor” (high-angular resolution) diffusion images, using metrics on Riemannian manifolds. Software implementing a wide range of algorithms will be demonstrated; tutorial notes will be provided. Talks will interest newcomers as well as experts in the field. Morning lectures on the principles behind the methods; afternoon lectures will go in-depth into applications.
Organizing Committee
Michael Miller (Johns Hopkins University, Center for Imaging Science)
Thomas Nichols (University of Oxford, GlaxoSmithKline Clinical Imaging Centre )
Russell Poldrack (University of California, Los Angeles (UCLA), Psychology)
Jonathan Taylor (Stanford University, Statistics)
Paul Thompson (University of California, Los Angeles (UCLA), Laboratory of NeuroImaging)
Keith Worsley (McGill University, Department of Mathematics and Statistics)
Website: http://www.ipam.ucla.edu/programs/mbi2008/
Schedule: https://www.ipam.ucla.edu/schedule.aspx?pc=mbi2008
In reference to his previous post , Thomas Colignatus writes:
There is a first draft of the first chapter of my intended book "Elementary statistics and causality" available at http://www.dataweb.nl/~cool/Papers/ESAC/Index.html
The question on the search co-authors might have been formulated a bit strict. I would welcome comments and questions a lot. Obviously, many authors have written on causality a lot, and it is kind of silly to put things in my very own words just to prevent issues on copyrights. On the other hand I am hesitant on full collaboration since the book would be programmed in Mathematica and I know that this is a kind of skill that is not available abundantly. Permissions to quote freely, with proper reference, would be ideal, and in the end you might simply appear to be a co-author. So, take a look at this first chapter and see if your work links up to it. The next chapter is to start with notions of conditional independence.
Thomas updates (5/30):
There is now a discussion "The 2 × 2 × 2 case in causality, of an effect, a cause and a confounder. A cross-over’s guide to the 2 × 2 × 2 contingency table" available at http://mpra.ub.uni-muenchen.de/3351/.
Thomas Colignatus writes:
I plan to write a book with the working title "Basics of causality, correlation, economics and epidemiology, using graphical models. Applications of Mathematica". This book would use Mathematica (www.wri.com) as an environment, so that the reader/user can directly experiment and simulate.
It would be handy to be in contact with other users of Mathematica and to have critical proof-readers in the process, to reduce confusion and increase user friendliness. If interested, send me an email at cool@dataweb.nl. If the input is important we might turn this into a collaboration.
I am currently following a course in graphical models, given by Richard Gill in Leiden, http://www.math.leidenuniv.nl/~gill/teaching/graphical/index.html, which together with Judea Pearl's "Causality" would give a good starting point.
For those who may be interested:
The Journal of Machine Learning Research (www.JMLR.org) is soliciting papers on all aspects of causality in machine learning, including theory, algorithms and applications. Papers making conceptual advances, making connections between various frameworks or disciplines, presenting novel methods appropriately supported by experiments and/or theory, or novel applications are particularly encouraged.
More details, including a list of suggested topics of interest and important dates are found at:
http://discover.mc.vanderbilt.edu/discover/public/jmlr_special_topic_causality.html
Guest editors are:
Constantin Aliferis, Vanderbilt University
Gregory Cooper, University of Pittsburgh
Andre Elisseeff, IBM Research
Isabelle Guyon, Clopinet
Peter Spirtes, Carnegie Mellon University
Thanks for visiting! We have been overwhelmed by the interest in our new blog, and we hope to continue improving this forum with your suggestions and comments (please keep them coming!) To our new visitors: we welcome you to browse through our archives and invite you to contribute any questions or opinions that you may have on the topic. Please keep in mind that while previous posts have been taken from discussions regarding the book "Causality" by Judea Pearl, the scope of this blog is not intended to be limited to a particular book or view. As we continue to develop the blog, you can expect to see new content including regular commentaries and topics of interest, conference/workshop announcements, abstracts/reviews of recent articles, and most importantly, your contributions. We hope to hear from you!
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