Papers
Accepted for Presentation
Identifiability of binary directed graphical models with hidden variables
Elizabeth Allman, John Rhodes, Elena Stanghellini, and Marco Valtorta
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo
A finite population test of the sharp null hypothesis for Compliers
Wen Wei Loh and Thomas Richardson
Reasoning about Independence in Probabilistic Models of Relational Data
Marc Maier, Katerina Marazopoulou, and David Jensen
A Sound and Complete Algorithm for Learning Causal Models from Relational Data
Marc Maier, Katerina Marazopoulou, David Arbour, and David Jensen
Maximum Likelihood estimation of structural nested logistic model with an instrumental variable
Roland Matsouaka and Eric Tchetgen Tchetgen
Single World Intervention Graphs: A Primer
Thomas Richardson and James Robins
Accepted for Posters
Scoring and Searching over Bayesian Networks with Informative, Causal and Associative Priors
Giorgos Borboudakis and Ioannis Tsamardinos
Learning Sparse Causal Models is not NP-hard
Tom Claasen, Joris Mooij and Tom Heskes
Why am I stuck? Causal Logic Models for Token-Level Causal Reasoning
Denver Dash, Mark Voortman and Martijn de Jongh
Bayesian Learning in Bayesian Networks of Moderate Size by Efficient Sampling
Ru He and Jin Tian
Sparse Nested Markov Models with Log-linear Parameters
Ilya Shpitser, Robin Evans, Thomas Richardson and James Robins
Student Posters
Maximum Likelihood Estimation in Cyclic Linear Gaussian Models with Correlated Errors
Christopher Fox and Mathias Drton
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
Eleni Sgouritsa, Dominik Janzing, Jonas Peters and Bernhard Schölkopf