Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments
Large language models (LLMs) are increasingly used to simulate human responses and estimate treatment effect of interventions when real-world experiments are costly or infeasible. The treatment-effect estimates are often evaluated using statistical realism, the degree to which simulated responses reproduce properties of observed human responses, although whether realism predicts treatment-effect accuracy remains unknown. Here we test this proxy relationship by jointly measuring statistical realism and treatment-effect accuracy on the same simulated responses in a cross-national experiment with 59,508 participants from 62 countries using three LLMs. The correlation between statistical real...
| Authors | Zonghan Li, Feng Ji |
|---|---|
| Primary category | cs.CY |
| Published | 2 April 2026 |
| Last updated at source | 27 July 2026 |
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