Abstract is missing.
- Preface: The 2019 ACM SIGKDD Workshop on Causal DiscoveryThuc Duy Le, Jiuyong Li, Kun Zhang 0001, Emre Kiciman, Peng Cui 0001, Aapo Hyvärinen. 1-3 [doi]
- Learning High-dimensional Directed Acyclic Graphs with Mixed Data-typesBryan Andrews, Joseph D. Ramsey, Gregory F. Cooper. 4-21 [doi]
- Scaling Causal Inference in Additive Noise ModelsKarim Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir. 22-33 [doi]
- Improve User Retention with Causal LearningShuyang Du, James Lee, Farzin Ghaffarizadeh. 34-49 [doi]
- Universal Causal Evaluation Engine: An API for empirically evaluating causal inference modelsAlexander Lin, Amil Merchant, Suproteem K. Sarkar, Alexander D'Amour. 50-58 [doi]
- Load-Balanced Parallel Constraint-Based Causal Structure Learning on Multi-Core Systems for High-Dimensional DataChristopher Schmidt, Johannes Huegle, Philipp Bode, Matthias Uflacker. 59-77 [doi]
- Detecting Social Influence in Event Cascades by Comparing Discriminative RankersSandeep Soni, Shawn Ling Ramirez, Jacob Eisenstein. 78-99 [doi]
- Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGsEric V. Strobl. 100-133 [doi]