Carnegie Mellon University

Workshop: Case Studies of Causal Discovery with Model Search

The Case Studies of Causal Discovery with Model Search Workshop focused on applications of causal model search to science. It included sessions on model search in Genetics, Biology, fMRI, Educational Research, Economics, and other disciplines.

Dates: October 25-27, 2013
Location: Carnegie Mellon University, Pittsburgh, PA

Tutorial on causal learning Richard Scheines [Slides]
Economics I David Bessler On Micro Economics I: The Use of TETRAD for Demand Specification [Video]
Economics II Kevin Hoover The Causal Structure of the Vector Autoregression in Economics: A Case Study [Slides]
Economics III Alessio Moneta Causal model search applied to economics: gains, pitfalls and challenges [Slides]
State of the art Frederick Eberhardt All of causal discovery [Slides]
Causality workbench Isabelle Guyon Causality workbench [Video]
fMRI I Joseph Ramsey & Clark Glymour Strategies for Discovering Mechanisms of Mind using fMRI [Slides]
fMRI II Catherine Hanson IMaGES in the Brain [Slides]
fMRI III Kathleen Gates Approaches for accommodating two problems inherent in causal discovery searches on functional MRI data. [Video]
fMRI IV Sergey Plis Brain Connectivity Analysis: from Unimodal to Multimodal [Slides]
Understanding climate dynamics Imme Ebert-Uphoff Two applications of causal discovery in climate science [Video]
Biology I Bill Shipley The worldwide leaf economic spectrum: How causal discovery algorithms forced me to re-imagine its generating causes. [Slides]
Biology II Ioannis Tsamardinos Causal Discovery from Mass Cytometry Data [Video]
Educational research I Richard Scheines Causal Models from Online Course and Tutor Logs [Slides]
Educational research II Martina Rau Searching for mediation models in intelligent tutoring systems data: representational understanding enhances representational fluency - but not vice versa [Slides]
Genetics I Marloes Maathuis Learning gene regulatory networks: instability of constraint-based causal structure learning methods [Video]
Genetics II Alexander Statnikov Active Learning of Local Causal Pathways from High-Dimensional Data: New Methods and Empirical Comparison
Genetics III Karen Sachs Biomolecular network models from single cell data [Video]
Commentary/Response Cosma Shalizi
Unsolved Problems Clark Glymour & Richard Scheines [Video]

Funding for this workshop was provided by the National Science Foundation, grant # SES1156001, and by the Center for Formal Epistemology at Carnegie Mellon University.