Transfer Learning via Functional Balancing in Reproducing Kernel Hilbert Spaces

As the availability of different data sources increases, transfer learning has become popular for improving estimation efficiency by incorporating those sources. In this paper, we propose a functional balancing transfer learning algorithm for observational studies integrating external summary information. We achieve efficiency improvement even when the model for the external summary information is misspecified, making the proposed algorithm more robust practically. The asymptotic properties of the proposed algorithm are investigated, and numerical studies demonstrate its advantages compared to alternative methods.