Federated Learning for Estimating Heterogeneous Treatment Effects

TL;DR

This paper introduces a federated learning framework for estimating heterogeneous treatment effects across multiple institutions, enabling privacy-preserving, collaborative, and personalized outcome prediction in healthcare and other domains.

Contribution

It proposes a novel federated learning approach that jointly learns shared feature representations and institution-specific predictive functions for heterogeneous treatment effects.

Findings

Effective on real-world clinical trial data

Handles heterogeneous input feature spaces

Enables privacy-preserving collaborative learning

Abstract

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.