Modeling Treatment Effect with Cross-Domain Data

Treatment effect estimation has received increasing attention recently. However, the issue of data sparsity often poses a significant challenge, limiting the feasibility of modeling. This paper aims to leverage cross-domain data to mitigate the data sparsity issue, and presents a framework called TEC. TEC incorporates a collaborative and adversarial generalization module to enhance information sharing and transferability across domains. This module encourages the learned representations of different domains to be more cohesive, thereby improving the generalizability of the models. Furthermore, we address the issue of poor performance for few-shot samples in each domain, and propose a pattern augmentation module that explicitly borrows samples from other domains and applies the self-teaching philosophy to them. Extensive experiments are conducted on both synthetic and benchmark datasets to demonstrate the superiority of the proposed framework.