Proceedings of Machine Learning Research

Volume 104: The 2019 ACM SIGKDD Workshop on Causal Discovery, 05 August 2019, Anchorage, Alaska, USA

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

Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kıcıman, Peng Cui, Aapo Hyvärinen; Proceedings of Machine Learning Research, PMLR 104:1-3

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

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

Improve User Retention with Causal Learning

Shuyang Du, James Lee, Farzin Ghaffarizadeh; Proceedings of Machine Learning Research, PMLR 104:34-49

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

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

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

Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs

Eric V. Strobl; Proceedings of Machine Learning Research, PMLR 104:100-133