Estimating the causal effects of medical treatments is difficult when clinically important outcomes are costly to measure or require long follow-up.
Short-term or inexpensive surrogate outcomes offer a potential alternative, but surrogate biomarkers may be unavailable or difficult to identify.
Advances in Artificial Intelligence
Advances in artificial intelligence (AI) have enabled increasingly accurate prediction of clinical outcomes from inexpensive, high-dimensional measurements, which creates an opportunity to use AI predictions themselves as surrogates.
Framework Development
To this end, we develop a framework for estimating treatment effects from paired measurements obtained before and after treatment for each treated individual.
A pretrained AI model is applied to the before and after measurements, and our estimator compares the resulting outcome predictions.
Technical Assumptions
We characterize the technical assumptions under which this within-person contrast identifies the average treatment effect on the treated, even when clinical outcomes are never observed for treated individuals.
Prediction-Powered Inference
When these assumptions cannot be justified, we use prediction-powered inference to correct bias using a small number of observed clinical outcomes and obtain valid inference.
Experiments and Validity
Synthetic and real-world cardio-oncology experiments demonstrate the validity and accuracy of the approach.