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.
Related papers
- Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
This thesis is a series of independent contributions to statistics unified by a model-free perspective.
The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.
The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
arXiv Detail & Related papers (2025-02-11T19:24:09Z) - Causal Lifting of Neural Representations: Zero-Shot Generalization for Causal Inferences [56.23412698865433]
We focus on causal inferences on a target experiment with unlabeled factual outcomes, retrieved by a predictive model fine-tuned on a labeled similar experiment.
First, we show that factual outcome estimation via Empirical Risk Minimization (ERM) may fail to yield valid causal inferences on the target population.
We propose Deconfounded Empirical Risk Minimization (DERM), a new simple learning procedure minimizing the risk over a fictitious target population.
arXiv Detail & Related papers (2025-02-10T10:52:17Z) - Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environments [1.4999444543328293]
Simulating learner actions helps stress-test open-ended interactive learning environments and prototype new adaptations before deployment.
We propose Hyp-Mix, a simulation authoring framework that allows experts to develop and evaluate simulations by combining testable hypotheses about learner behavior.
arXiv Detail & Related papers (2024-10-03T00:25:40Z) - Simulating Field Experiments with Large Language Models [0.6144680854063939]
This paper pioneers the utilization of large language models (LLMs) for simulating field experiments.
By introducing two novel prompting strategies, observer and participant modes, we demonstrate the ability of LLMs to both predict outcomes and replicate participant responses within complex field settings.
Our findings indicate a promising alignment with actual experimental results in certain scenarios, achieving a stimulation accuracy of 66% in observer mode.
arXiv Detail & Related papers (2024-08-19T03:41:43Z) - LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations.
We introduce Scientific Generative Agent (SGA), a bilevel optimization framework.
We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
arXiv Detail & Related papers (2024-05-16T03:04:10Z) - LLM-driven Imitation of Subrational Behavior : Illusion or Reality? [3.2365468114603937]
Existing work highlights the ability of Large Language Models to address complex reasoning tasks and mimic human communication.
We propose to investigate the use of LLMs to generate synthetic human demonstrations, which are then used to learn subrational agent policies.
We experimentally evaluate the ability of our framework to model sub-rationality through four simple scenarios.
arXiv Detail & Related papers (2024-02-13T19:46:39Z) - Online simulator-based experimental design for cognitive model selection [74.76661199843284]
We propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods.
In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to 2 orders of magnitude less time than existing LFI alternatives.
arXiv Detail & Related papers (2023-03-03T21:41:01Z) - Sequential Causal Imitation Learning with Unobserved Confounders [82.22545916247269]
"Monkey see monkey do" is an age-old adage, referring to na"ive imitation without a deep understanding of a system's underlying mechanics.
This paper investigates the problem of causal imitation learning in sequential settings, where the imitator must make multiple decisions per episode.
arXiv Detail & Related papers (2022-08-12T13:53:23Z) - On the Importance of Application-Grounded Experimental Design for
Evaluating Explainable ML Methods [20.2027063607352]
We present an experimental study extending a prior explainable ML evaluation experiment and bringing the setup closer to the deployment setting.
Our empirical study draws dramatically different conclusions than the prior work, highlighting how seemingly trivial experimental design choices can yield misleading results.
We believe this work holds lessons about the necessity of situating the evaluation of any ML method and choosing appropriate tasks, data, users, and metrics to match the intended deployment contexts.
arXiv Detail & Related papers (2022-06-24T14:46:19Z) - Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown.
We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
arXiv Detail & Related papers (2021-11-02T12:33:42Z) - Simulation as Experiment: An Empirical Critique of Simulation Research
on Recommender Systems [4.006331916849688]
We argue that simulation studies of recommender system (RS) evolution are conceptually similar to empirical experimental approaches.
By adopting standards and practices common in empirical disciplines, simulation researchers can mitigate many of these weaknesses.
arXiv Detail & Related papers (2021-07-29T21:05:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.