The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective

Understand

Large Language Models (LLMs) have shown impressive potential to simulate human behavior.

• We identify a fundamental challenge in using them to simulate experiments: when LLM-simulated subjects are blind to the experimental design (as is standard practice with human subjects), variations in treatment systematically affect unspecified variables that should remain constant, violating the unconfoundedness assumption.
• Using demand estimation as a context and an actual experiment with 40 different products as a benchmark, we show this can lead to implausible results.
• While confounding may in principle be addressed by controlling for covariates, this can compromise ecological validity in the context of LLM simulations: controlled covariates become artificially salient in the simulated decision process.