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

Abstract

Modern marketing increasingly requires managers to deploy new content at scale, often with limited opportunity for prior testing. As a result, decisions about what to launch become strategic managerial choices under uncertainty rather than purely creative exercises. While generative AI makes the creation of new content fast and highly scalable, it simultaneously expands the set of options managers must evaluate, making reliable content selection increasingly difficult. We develop a framework for causal prediction that enables managers to evaluate and deploy novel marketing content generated by AI. The framework uses pretrained large language models to represent previously deployed content and learn how its features causally relate to outcomes. Using a rejection-sampling procedure, the framework screens new content proposed by generative AI to avoid extrapolation beyond what historical data can reliably support. In a large-scale email marketing application (3.3 million observations across 34 campaigns), the framework improves out-of-sample prediction and real-world deployment performance relative to standard approaches, enabling outcome-guided generation of higher-performing AI-generated content. The framework establishes a threshold based on how closely new content resembles past campaigns, separating cases where causal prediction is reliable from cases where direct experimentation is warranted. The framework has important implications for marketing decision making in a rapidly evolving environment where generative AI is transforming content creation and deployment.