Using Causal Inference to Solve Uncertainty Issues in Dataset Shift
Pages 1155 - 1157
Abstract
Dataset shift will lead to uncertainty issues, and then the models will accurately decline. Using causality instead of correlation to find the invariant characteristic and solve the uncertainty issues between different dataset distributions (eg. Domain Adaptation). Summarizing datasets can be used in current domain training, building a benchmarking framework of causal learning that combines the causal inference and traditional model to detect, address, and determine the characteristic of the dataset shift.
Supplementary Material
Dataset shift will lead to uncertainty issues, and then the models will accurately decline. We should use causal inference to improve this problem. In this vedio we will introduce two questions: 1) Why we need causal inference in dataset shift? 2)What can causal inference do in dataset shift?
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References
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Published In
March 2024
1246 pages
ISBN:9798400703713
DOI:10.1145/3616855
- General Chairs:
- Luz Angélica,
- Silvio Lattanzi,
- Andrés Muñoz Medina,
- Program Chairs:
- Leman Akoglu,
- Aristides Gionis,
- Sergei Vassilvitskii
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Published: 04 March 2024
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WSDM '24: The 17th ACM International Conference on Web Search and Data Mining
March 4 - 8, 2024
Merida, Mexico
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