The Challenge of Using LLMs to Simulate Human Behavior: A Causal
Inference Perspective
- URL: http://arxiv.org/abs/2312.15524v1
- Date: Sun, 24 Dec 2023 16:32:35 GMT
- Title: The Challenge of Using LLMs to Simulate Human Behavior: A Causal
Inference Perspective
- Authors: George Gui, Olivier Toubia
- Abstract summary: Large Language Models (LLMs) have demonstrated impressive potential to simulate human behavior.
We show that variations in the treatment included in the prompt can cause variations in unspecified confounding factors.
We propose a theoretical framework suggesting this endogeneity issue generalizes to other contexts.
- Score: 0.32634122554913997
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) have demonstrated impressive potential to
simulate human behavior. Using a causal inference framework, we empirically and
theoretically analyze the challenges of conducting LLM-simulated experiments,
and explore potential solutions. In the context of demand estimation, we show
that variations in the treatment included in the prompt (e.g., price of focal
product) can cause variations in unspecified confounding factors (e.g., price
of competitors, historical prices, outside temperature), introducing
endogeneity and yielding implausibly flat demand curves. We propose a
theoretical framework suggesting this endogeneity issue generalizes to other
contexts and won't be fully resolved by merely improving the training data.
Unlike real experiments where researchers assign pre-existing units across
conditions, LLMs simulate units based on the entire prompt, which includes the
description of the treatment. Therefore, due to associations in the training
data, the characteristics of individuals and environments simulated by the LLM
can be affected by the treatment assignment. We explore two potential
solutions. The first specifies all contextual variables that affect both
treatment and outcome, which we demonstrate to be challenging for a
general-purpose LLM. The second explicitly specifies the source of treatment
variation in the prompt given to the LLM (e.g., by informing the LLM that the
store is running an experiment). While this approach only allows the estimation
of a conditional average treatment effect that depends on the specific
experimental design, it provides valuable directional results for exploratory
analysis.
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