2 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
Logging Policy Design for Off-Policy Evaluation
Connor Douglas, Joel Persson, Foster Provost
Off-policy evaluation (OPE) estimates the value of a target treatment policy (e.g., a recommender system) using data collected by a different logging policy. It enables high-stakes…
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…