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SUMMARY:Adventures in Demand Analysis Using AI - Victor Chernozhukov (Mass
 achusetts Institute of Technology)
DTSTART:20260127T160000Z
DTEND:20260127T170000Z
UID:TALK241567@talks.cam.ac.uk
DESCRIPTION:This paper advances empirical demand analysis by integrating m
 ultimodal product representations derived from artificial intelligence (AI
 ). Using a detailed dataset of toy cars on \\textit{this http URL}\, we co
 mbine text descriptions\, images\, and tabular covariates to represent eac
 h product using transformer-based embedding models. These embeddings captu
 re nuanced attributes\, such as quality\, branding\, and visual characteri
 stics\, that traditional methods often struggle to summarize. Moreover\, w
 e fine-tune these embeddings for causal inference tasks. We show that the 
 resulting embeddings substantially improve the predictive accuracy of sale
 s ranks and prices and that they lead to more credible causal estimates of
  price elasticity. Notably\, we uncover strong heterogeneity in price elas
 ticity driven by these product-specific features. Our findings illustrate 
 that AI-driven representations can enrich and modernize empirical demand a
 nalysis. The insights generated may also prove valuable for applied causal
  inference more broadly.
LOCATION:Seminar Room 1\, Newton Institute
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