This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on textit{Amazon.com}, we combine text descriptions, images, and tabular covariates to represent each product using transformer-based embedding models. These embeddings capture nuanced attributes, such as quality, branding, and visual characteristics, that traditional methods often struggle to summarize. Moreover, we fine-tune these embeddings for causal inference tasks. We show that the resulting embeddings substantially improve the predictive accuracy of sales ranks and prices and that they lead to more credible causal estimates of price elasticity. Notably, we uncover strong heterogeneity in price elasticity driven by these product-specific features. Our findings illustrate that AI-driven representations can enrich and modernize empirical demand analysis. The insights generated may also prove valuable for applied causal inference more broadly.

翻译：本文通过整合源自人工智能（AI）的多模态产品表征，推进了实证需求分析的研究。利用来自 textit{Amazon.com} 的玩具汽车详细数据集，我们结合文本描述、图像和表格协变量，使用基于 Transformer 的嵌入模型来表征每个产品。这些嵌入捕捉了传统方法难以概括的细微属性，例如质量、品牌和视觉特征。此外，我们针对因果推断任务对这些嵌入进行了微调。结果表明，所得的嵌入显著提高了对销售排名和价格的预测准确性，并带来了更可信的价格弹性因果估计。值得注意的是，我们发现了由这些产品特定特征驱动的价格弹性存在显著的异质性。我们的发现表明，AI 驱动的表征能够丰富并现代化实证需求分析。所产生的见解也可能在更广泛的应用因果推断领域证明其价值。