Abstract is missing.
- Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and DecisionThuc Duy Le, Jiuyong Li, Robert Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui 0001, Kun Kuang, Jian Pei, Fei Wang 0001, Mattia Prosperi. 1-2 [doi]
- Causally Learning an Optimal Rework PolicyOliver Schacht, Sven Klaassen, Philipp Schwarz, Martin Spindler, Daniel Grünbaum, Sebastian Imhof. 3-24 [doi]
- Leveraging covariate adjustments at scale in online A/B testingLorenzo Masoero, Doug Hains, James McQueen. 25-48 [doi]
- Stable Prediction on Graphs with Agnostic Distribution ShiftsShengyu Zhang 0001, Yunze Tong, Kun Kuang, Fuli Feng, Jiezhong Qiu, Jin Yu, Zhou Zhao, Hongxia Yang, Zhongfei Zhang, Fei Wu 0001. 49-74 [doi]
- Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided SolutionWang Lu, Jindong Wang 0001, Yidong Wang, Xing Xie 0001. 75-97 [doi]
- Optimizing Dynamic Antibiotic Treatment Strategies against Invasive Methicillin-Resistant Staphylococcus Aureus Infections using Causal Survival Forests and G-Formula on Statewide Electronic Health Record DataInyoung Jun, Scott A. Cohen, Sarah E. Ser, Simone Marini, Robert J. Lucero, Jiang Bian 0001, Mattia Prosperi. 98-115 [doi]
- Bias-Variance Tradeoffs for Designing Simultaneous Temporal ExperimentsRuoxuan Xiong, Alex Chin, Sean Taylor. 115-131 [doi]