Title: Tetrad: Machine Learning and Graphical Causal Models
Abstract: Tetrad is an integrated suite of software for specifying, estimating, and
searching for graphical causal models. It contains over 20 algorithms for searching for a
variety of model classes, e.g., path analytic models, Bayes nets, factor analytic and
structural equation models, general latent variable models of conditional independence
structures, and Markov blankets. In this workshop, we will briefly introduce graphical
causal models, show how to build, simulate data from, and estimate such models in
Tetrad, explain model search, teach how to use a variety of the search algorithms in
Tetrad on real and simulated data, and go through several more extended case studies in
which Tetrad is applied to real data, including fMRI data to find causal relations between
brain regions.
Course Outline
Who Should Attend
Prerequisites
What you will learn
Software Note
Speaker Biographies
Richard Scheines is the Head of Philosophy, with joint appointments in Machine
Learning and Human-Comptuer Interaction at Carnegie Mellon University. Along with
Peter Spirtes and Clark Glymour, Scheines has published several books and dozens of
article on graphical causal models over the last 20 years. He has used model search in
Tetrad to model the effect of lead on IQ, the effect of online courseware on student
learning, and several other social scientific questions. Scheines has also served on
several IOM committees reviewing the statistical evidence for causation in veteran's
disability claims and for the effect of food marketing on childhood obesity.
Joseph Ramsey is a PhD in Philosophy of Science from the University of California, San
Diego, and is currently the Director of Research Computing at Carnegie Mellon's Lab for
Symbolic and Educational Computing, as well as the chief developer of the Tetrad
programs. Ramsey has done extensive algorithm development and applied work in causal
modeling, including spectral identification of carbonate content, and most recently the
identification of causal pathways in brain processing from fMRI data, work that is
recently published in NeuroImage.