Advances in Causal Inference
Thursday July 16th, 2015
Amsterdam, Netherlands
Advances in Causal Inference was a workshop that took
place immediately after the 31st Conference on Uncertainty in
Artificial Intelligence (UAI
2015).
Workshop Proceedings
The proceedings have been published through the CEUR Workshop Proceedings series and are now available at this link. We also provide a BiBTeX file for convenience.
Draft Papers and Abstracts
The papers and abstracts below are drafts. It includes some abstracts not present in the proceedings but presented at the workshop.Causal and Statistical Inference with Social Network Data: Massive Challenges and Meager Progress (invited paper)
Elizabeth Ogburn
Causal Reasoning for Events in Continuous Time: A Decision-Theoretic Approach (invited paper)
Vanessa Didelez
Learning the Structure of Causal Models with Relational and Temporal Dependence
Katerina Marazopoulou, Marc Meier and David Jensen
Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates
Babak Salimi and Leopoldo Bertossi
Causal Interpretation Rules for Encoding and Decoding Models in Neuroimaging
Sebastian Weichwald, Timm Meyer, Ozan Özdenizci, Bernhard Schölkopf, Tonio Ball and Moritz Grosse-Wentrup
Inference of Cause and Effect with Unsupervised Inverse Regression
Eleni Sgouritsa, Dominik Janzing, Philipp Hennig and Bernhard Schölkopf
Exploiting Causality for Efficient Monitoring in POMDPs
Stefano V. Albrecht and Subramanian Ramamoorthy
An Empirical Study of the Simplest Causal Prediction Algorithm
Jerome Cremers and Joris Mooij
Visual Causal Feature Learning
Krzysztof Chalupka, Pietro Perona and Frederick Eberhardt
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning
Sanghack Lee and Vasant Honavar
Robust reconstruction of causal graphical models based on conditional 2-point and 3-point information
Séverine Affeldt and Hervé Isambert
An Algorithm to Compute the Likelihood Ratio Test Statistic of the Sharp Null Hypothesis for Compliers
Wen Wei Loh and Thomas S. Richardson
Segregated Graphs and Marginals of Chain Graph Models
Ilya Shpitser
Recovering from Selection Bias using Marginal Structure in Discrete Models
Robin J. Evans and Vanessa Didelez
Advances in Integrative Causal Analysis
Ioannis Tsamardinos