- By:
- Cho, Wendy; Liu, Yan
- Page Number:
- 167-171
- Book Title:
- ICPP Workshops '23: Proceedings of the 52nd International Conference on Parallel Processing Workshops
- Publication Date:
- March 12, 2026
- Publisher Location:
- Association for Computing Machinery, New York, New York, United States of America
- Conference Name:
- The 13th International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS 2023)
- Conference Location:
- Salt Lake City, Utah, United States of America
- Conference Sponsor:
- ACM
- View DOI Listing:
- https://doi.org/10.1145/3605731.3608930
Abstract
We develop a GPU-accelerated machine learning generative adversarial network model that can be used with observational data for the purpose of constructing causal inferences. The theoretical basis of our machine learning model is novel and is conceptualized to be operable and scalable for high performance computing platforms. Our GPU-accelerated code enables large-scale parallelization of the computation within a common and accessible computing environment. This will expand the reach of our model and empower research in new substantive domains while maintaining the underlying theoretical properties.
