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ORCIDCD@KDD 2019: Anchorage, Alaska, USA
- Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2019, Anchorage, Alaska, USA, August 5, 2019. Proceedings of Machine Learning Research 104, PMLR 2019 - Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. 1-3 - Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper:
Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types. 4-21 - Charles K. Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir:
Scaling Causal Inference in Additive Noise Models. 22-33 - Shuyang Du, James Lee, Farzin Ghaffarizadeh:
Improve User Retention with Causal Learning. 34-49 - Alexander Lin, Amil Merchant, Suproteem K. Sarkar, Alexander D'Amour:
Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models. 50-58 - Christopher Schmidt, Johannes Huegle, Philipp Bode, Matthias Uflacker:
Load-Balanced Parallel Constraint-Based Causal Structure Learning on Multi-Core Systems for High-Dimensional Data. 59-77 - Sandeep Soni, Shawn Ling Ramirez, Jacob Eisenstein:
Detecting Social Influence in Event Cascades by Comparing Discriminative Rankers. 78-99 - Eric V. Strobl:
Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs. 100-133
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