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UAI 2017

Schedule

08:45	Welcome and Opening Remarks
09:00	Invited Talk 09:00 Algorithmic bias & other human-centric challenges in AI David Danks (CMU)
	Workshop papers 10:30 Causal Consistency of Structural Equation Models Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schoelkopf
10:30	Coffee Break & Posters
11:00	Workshop papers 11:00 Causal Discovery in the Presence of Measurement Noise: Identifiability Conditions Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour 11:30 SAT-Based Causal Discovery under Weaker Assumptions Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer
12:00	Lunch & Poster Session
14:00	Invited talk 14:00 Towards a Decision-Theoretic Foundation for (Imprecise) Interventional Probabilities Jiji Zhang (Lingnan University)
	Workshop papers 15:00 Algebraic Equivalence of Linear Structural Equation Models Thijs van Ommen, Joris M. Mooij
15:30	Coffee Break & Posters
16:00	Workshop papers 16:00 Counting Markov Equivalence Classes by Number of Immoralities Liam Thomas Solus, Caroline Uhler, Adit Radhakrishnan 16:30 Probabilistic Active Learning of Functions in Structural Causal Models Paul K Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin and Philipp Hennig 17:00 Learning Dynamic Structure from Undersampled Data John W. Cook, David Danks, Sergey M. Plis
	Causality in sister conferences (posters + short talks) 17:30 (UAI-17) Self-Discrepancy Conditional Independence Test & A Kernel Conditional Independence Test for Relational Data Sanghack Lee, Vasant Honavar 17:44 (ICML-17) Identifying Best Interventions through Online Importance Sampling Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai 17:58 (UAI-17) Interpreting and using CPDAGs with background knowledge Emilija Perkovic, Markus Kalisch, Marloes H. Maathuis 18:12 (ICML-17) Deep IV: A Flexible Approach for Counterfactual Prediction Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy 18:26 (UAI-17) Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges Marco Eigenmann, Preetam Nandy, Marloes Maathuis
18:40	Closing remarks