A GPU‐Accelerated Generative Adversarial Model for Causal Inference

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.