CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation

Abstract

Treatment effect estimation (TEE) aims to identify the causal effects of treatments on important outcomes. Current machine-learning-based methods, mainly trained on labeled data for specific treatments or outcomes, can be sub-optimal with limited labeled data. In this article, we propose a new pre-training and fine-tuning framework, CURE (causal treatment effect estimation), for TEE from observational data. CURE is pre-trained on large-scale unlabeled patient data to learn representative contextual patient representations and fine-tuned on labeled patient data for TEE. We present a new sequence encoding approach for longitudinal patient data embedding both structure and time. Evaluated on four downstream TEE tasks, CURE outperforms the state-of-the-art methods, marking a 7% increase in area under the precision-recall curve and an 8% rise in the influence-function-based precision of estimating heterogeneous effects. Validation with four randomized clinical trials confirms its efficacy in producing trial conclusions, highlighting CURE's capacity to supplement traditional clinical trials.