Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost. We study when a treatment effect estimated on LLM outcomes can recover the effect for the human population of interest. Distributional equivalence between LLM and human outcomes would make any standard estimator valid but is unrealistic. We therefore develop a statistical framework that adapts surrogate endpoint theory to LLMs, showing that calibrating LLM outcomes to human outcomes identifies the average treatment effect under surrogacy and comparability conditions that are jointly weaker than dist...
| Authors | Joel Persson, Mårten Schultzberg, Sebastian Ankargren |
|---|---|
| Primary category | stat.ME |
| Published | 15 June 2026 |
| Last updated at source | 23 June 2026 |
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