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This work develops Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization and Generative Engine Marketing and investigates the empirical performance of the proposed method using simulated answers to product recommendation in English and Japanese.
RAG-based policy learning can significantly enhance causal inference by effectively integrating nearest-neighbor matching for action selection.
