  Machine learning methods for estimating heterogeneous treatment effects (HTE)
facilitate large-scale personalized decision-making across various domains such
as healthcare, policy making, education, and more. Current machine learning
approaches for HTE require access to substantial amounts of data per treatment,
and the high costs associated with interventions makes centrally collecting so
much data for each intervention a formidable challenge. To overcome this
obstacle, in this work, we propose a novel framework for collaborative learning
of HTE estimators across institutions via Federated Learning. We show that even
under a diversity of interventions and subject populations across clients, one
can jointly learn a common feature representation, while concurrently and
privately learning the specific predictive functions for outcomes under
distinct interventions across institutions. Our framework and the associated
algorithm are based on this insight, and leverage tabular transformers to map
multiple input data to feature representations which are then used for outcome
prediction via multi-task learning. We also propose a novel way of federated
training of personalised transformers that can work with heterogeneous input
feature spaces. Experimental results on real-world clinical trial data
demonstrate the effectiveness of our method.
