EXPRESS: Causal Alignment: Augmenting Language Models with A/B Tests

We develop a framework for improving the effectiveness of marketing communications by aligning a language model to the causal signal in A/B tests. The core idea is that randomized experiments reveal not just which content performs best, but directions of improvement that can serve as supervision for a generative model. Using within-experiment comparisons, we fine-tune a language model to create an improver architecture that transforms lower-performing content into higher-performing content. The resulting model can be composed with any baseline decision-maker, e.g. a human expert, to suggest improvements to new content. We validate our framework through a large-scale field experiment in email marketing spanning 36 new campaigns and 283 million impressions. Subject lines improved by our aligned model achieve click-through rates 32% higher than those from unassisted human experts, while a general-purpose language model with 30 times as many parameters as our fine-tuned model does not improve outcomes. This suggests that alignment to experimental data adds value beyond model capabilities alone. Our findings provide the first field evidence that aligning language models to A/B tests improves outcomes in real marketing campaigns and show that integrating AI with experimentation can train systems that make better future decisions.