Work

A GPU‐Accelerated Generative Adversarial Model for Causal Inference

Year: 2025

Type: article

Abstract: ABSTRACT We develop a GPU‐accelerated machine learning generative adversarial model designed to facilitate causal inferences from observational data. Our model's theoretical framework is conceptualized in a manner that is amenable to being operable and scalable for high‐performance computing platforms. We leverage GPU acceleration to develop a parallel evolutionary algorithm to achieve large‐scale parallel computation of the model within a now widely accessible computing platform. This capability both enhances computational speedup and efficiency and also extends the use of the model to a broader range of substantive research domains while maintaining the underlying theoretical properties of the model.

Source: Concurrency and Computation Practice and Experience

DOI: 10.1002/cpe.70231

Authors: Wendy K. Tam, Yan Y. Liu

Institutions: Vanderbilt University, Oak Ridge National Laboratory

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $5,140

Funder: Oak Ridge National Laboratory