confounding

This paper from Google DeepMind and Carnegie Mellon argues that LLM-simulated experiments are actually observational studies due to user drift, where the simulated population shifts with interventions. The authors propose using negative control outcomes to diagnose confounding and show that eliciting setting-relevant confounders can reduce bias.