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stat.ME2026
Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
Joel Persson, Mårten Schultzberg, Sebastian Ankargren
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…
stat.ME2026
Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Joel Persson, Jurriën Bakker, Dennis Bohle +2
Heterogeneous treatment effects (HTEs) are increasingly estimated using machine learning models that produce highly personalized predictions of treatment effects. In practice, howe…