Causal Value Attribution of Marketing Labels in LLM-Generated Ads: A Deconfounder Approach

The paper introduces D‑VAE, a Deconfounder Variational Autoencoder that estimates causal effects of expert‑generated marketing labels on LLM‑generated advertisements. By integrating a multi‑treatment deconfounder with causal representation learning, D‑VAE disentangles expert‑labeling logic and provides PAC‑based bounds on omitted‑variable bias. Applied to over 29,000 foundation‑makeup ad creatives, the method outperforms existing benchmarks and a field experiment shows that its recommended label configurations boost engagement by 32.7% while matching human expert performance.

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• llms
• multimodal
• benchmarks
• safety