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August 5, 2026Journal of Marketing Research

EXPRESS: Evaluating Novel Unstructured Treatments with Generative AI: A Causal Prediction Framework

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Authors

PEPaul B. EllicksonWKWreetabrata KarJRJames C. Reeder

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Overview

Randomized trial evaluates a causal prediction framework for AI-generated marketing content, suggesting improved decisions under uncertainty.

Key Points

  • The research aims to establish a causal prediction framework that helps managers evaluate AI-generated marketing content under uncertainty.
  • Developed a causal prediction framework utilizing pretrained large language models to analyze past marketing campaigns.
  • Implemented a rejection-sampling procedure to screen new AI-generated content based on historical data.
  • Applied the framework in a large-scale email marketing application with 3.3 million observations across 34 campaigns.
  • The framework improved out-of-sample prediction performance compared to standard methodologies.
  • Outcome-guided generation led to the creation of higher-performing AI-generated content.
  • The framework effectively identified reliable cases for causal prediction versus those requiring experimentation.

Cite This Study

Ellickson et al. (2026) studied this question.

synapsesocial.com/papers/6a72e85b226790f370658225https://doi.org/10.1177/00222437261476639
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