Causation, Prediction and Search +20

Attention conservation notice: Log-rolling promotion a conference at the intersection of the margins of several academic fields.

I've written before about how one of Causation, Prediction and Search was one of the books which awakened my interest in machine learning and modern statistics. The point of the book was to explore when and how one can actually discover causal relations from observations. The CMU philosophy department being what it is, they did this by devising computational representations of causal structure, and effective procedures for causal discovery, and proving that the procedures would work under specific (sane) conditions. This message has shaped my research and my teaching ever since. It's one of the reasons I was so eager to come to CMU.

Of course, for good pragmatists, the proof of any method is in its results, and that's why I'm very pleased to help publicize this:

Case Studies of Causal Discovery with Model Search

Description: Computer scientists, statisticians, and philosophers have created a precise mathematical framework for representing causal systems called "Graphical Causal Models." This framework has supported the rigorous description of causal model spaces and the notion of empirical indistinguishability/equivalence within such spaces, which has in turn enabled computer scientists to develop asymptotically reliable model search algorithms for efficiently searching these spaces. The conditions under which these methods are practically useful in applied science is the topic of this workshop. The workshop will bring together scholars from genetics, biology, economics, fMRI-based cognitive neuroscience, climate research, education research, and several other disciplines, all of whom have successfully applied computerized search for causal models toward a scientifically challenging problem. The goals for the workshop are to: (1) to identify strategies for applying causal model search to diverse domain-specific scientific questions; (2) to identify and discuss methodological challenges that arise when applying causal model search to real-world scientific problems; and (3) to take concrete steps toward creating an interdisciplinary community of researchers interested in applied causal model search. We welcome junior scholars and graduate students, and we will host a free introductory tutorial on model search the first morning of the workshop.

Time and place: 25--27 October 2013, Carnegie Mellon University

(Yet another sign of the passage of time is that one of the organizers is Lizzie Silver, who helped perpetrate this when she took 36-350.)

Constant Conjunction Necessary Connexion; Kith and Kin