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