Artificial intelligence surrogates for treatment effect estimation with before-and-after data
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 (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. To this end, we develop a framework for estimating treatment effects from paired measurements obtained before and after treatment for each treated individual. A pretrai...
| Authors | Frances Dean, Anna Neufeld, Joshua Barrios et al. |
|---|---|
| Primary category | stat.ML |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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