Vol-1792
urn:nbn:de:0074-1792-5

Copyright © 2016 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

UAI-CFA 2016
UAI 2016 Workshop on Causation: Foundation to Application

Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application
co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)

Jersey City, USA, June 29, 2016.

Edited by

Frederick Eberhardt *
Elias Bareinboim
Marloes Maathuis *
Joris Mooij
Ricardo Silva *

* California Institute of Technology, Division of the Humanities and Social Sciences, Pasadena, USA
** Purdue University, Department of Computer Science, West Lafayette, USA
*** ETH Zürich, Seminar für Statistik, Zürich, Switzerland
**** University of Amsterdam, Informatics Institute, Amsterdam, The Netherlands
***** University College London, Department of Statistical Science, and The Alan Turing Institute, London, UK

Table of Contents

Preface

Invited Papers

Measurement Error and Causal Discovery (invited paper) 1-7
Richard Scheines, Joseph Ramsey
Dynamical Kinds and their Discovery (invited paper) 8-17
Benjamin C. Jantzen

Contributed Papers

Causal Inference by Minimizing the Dual Norm of Bias: Kernel Matching & Weighting Estimators for Causal Effects 18-28
Nathan Kallus
Pairwise Cluster Comparison for Learning Latent Variable Models 29-38
Nuaman Asbeh, Boaz Lerner
Marginal Causal Consistency in Constraint-based Causal Learning 39-47
Anna Roumpelaki, Giorgos Borboudakis, Sofia Triantafillou, Ioannis Tsamardinos
Separating Sparse Signals from Correlated Noise in Binary Classification 48-58
Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft
Score-based vs Constraint-based Causal Learning in the Presence of Confounders 59-67
Sofia Triantafillou, Ioannis Tsamardinos

2017-02-07: submitted by Frederick Eberhardt, metadata incl. bibliographic data published under Creative Commons CC0
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