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March 31, 2026Open Access

Causal Estimation for Online Experiments Using LLM Shopper Agents

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Authors

HCHanuma Ramesh Chadalavada

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Overview

A causal-inference framework evaluates online experiments using LLM shopper agents, highlighting validation needs.

Key Points

  • This research aims to establish a causal framework for online experiments using LLM shopper agents to enhance pre-launch testing.
  • Developed a dynamic potential-outcomes setting for simulated randomized controlled trials.
  • Utilized memory-driven behavior and explicit exposure models in simulations.
  • Applied methods such as population transport and doubly robust treatment-effect estimation.
  • Conducted falsification tests and uncertainty decomposition to assess outputs.
  • Directional alignment observed with a real experiment indicates validity of the model.
  • Identified practical failure modes in simulations that necessitate careful validation before implementation.

Cite This Study

Hanuma Ramesh Chadalavada (2026) studied this question.

synapsesocial.com/papers/69cb6526e6a8c024954b940ehttps://doi.org/10.5281/zenodo.19323812
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