About

I am an Associate Professor at the University of Chicago Booth School of Business. A key objective of my research is to enhance the reliability of empirical analyses in quantitative marketing and industrial organization. To achieve this, I develop methods that relax restrictive assumptions about economic agents' behavior and/or integrate novel data sources, including unstructured data.

Research topics

• Economics
• Computer Science
• Econometrics
• Computer Security
• Microeconomics
• Engineering
• Mathematical analysis
• Monetary economics
• Operations management
• Cognitive psychology
• Business
• Social psychology
• Financial economics
• Psychology
• Transport engineering
• Finance
• Mathematics

Selected publications

• A Method to Estimate Discrete Choice Models That Is Robust to Consumer Search

  Journal of Political Economy · 2026-01-07 · 1 citations

  articleOpen access

  We state a sufficient condition under which choice data alone suffices to identify consumer preferences when choices are not fully informed. Suppose that: (i) the data generating process is a search model in which the attribute hidden to consumers is observed by the econometrician; (ii) if a consumer searches good j, she also searches goods which are better than j in terms of the non-hidden component of utility; and (iii) consumers choose the good that maximizes overall utility among searched goods. Canonical models will be biased: the value of the hidden attribute will be understated because consumers will be unresponsive to variation in the attribute for goods that they do not search. Under the conditions above and additional mild restrictions, an alternative method of recovering preferences using cross derivatives of choice probabilities succeeds regardless of the search protocol and is thus robust to whether consumers are informed. The approach nests several standard models, including full information. Our methods suggest natural tests for full information and can be used to forecast how consumers will respond to additional information. We verify in a lab experiment that our approach succeeds in recovering preferences when consumers engage in costly search.

  PublisherOA PDFDOI

• From Unstructured Data to Demand Counterfactuals: Theory and Practice

  ArXiv.org · 2026-01-08

  articleOpen accessSenior author

  Empirical models of demand for differentiated products rely on low-dimensional product representations to capture substitution patterns. These representations are increasingly proxied by applying ML methods to high-dimensional, unstructured data, including product descriptions and images. When proxies fail to capture the true dimensions of differentiation that drive substitution, standard workflows will deliver biased counterfactuals and invalid inference. We develop a practical toolkit that corrects this bias and ensures valid inference for a broad class of counterfactuals. Our approach applies to market-level and/or individual data, requires minimal additional computation, is efficient, delivers simple formulas for standard errors, and accommodates data-dependent proxies, including embeddings from fine-tuned ML models. It can also be used with standard quantitative attributes when mismeasurement is a concern. In addition, we propose diagnostics to assess the adequacy of the proxy construction and dimension. The approach yields meaningful improvements in predicting counterfactual substitution in both simulations and an empirical application.

  PublisherOA PDF

• From Unstructured Data to Demand Counterfactuals: Theory and Practice

  arXiv (Cornell University) · 2026-01-08

  preprintOpen accessSenior author

  Empirical models of demand for differentiated products rely on low-dimensional product representations to capture substitution patterns. These representations are increasingly proxied by applying ML methods to high-dimensional, unstructured data, including product descriptions and images. When proxies fail to capture the true dimensions of differentiation that drive substitution, standard workflows will deliver biased counterfactuals and invalid inference. We develop a practical toolkit that corrects this bias and ensures valid inference for a broad class of counterfactuals. Our approach applies to market-level and/or individual data, requires minimal additional computation, is efficient, delivers simple formulas for standard errors, and accommodates data-dependent proxies, including embeddings from fine-tuned ML models. It can also be used with standard quantitative attributes when mismeasurement is a concern. In addition, we propose diagnostics to assess the adequacy of the proxy construction and dimension. The approach yields meaningful improvements in predicting counterfactual substitution in both simulations and an empirical application.

  PublisherDOI

• Demand Estimation with Text and Image Data

  arXiv (Cornell University) · 2025-03-26

  preprintOpen access1st authorCorresponding

  We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand estimation even when researchers lack data on product attributes or when consumers value hard-to-quantify attributes such as visual design. Using a choice experiment, we show this approach substantially outperforms standard attribute-based models at counterfactual predictions of second choices. We also apply it to 40 product categories offered on Amazon.com and consistently find that unstructured data are informative about substitution patterns.

  PublisherOA PDFDOI

• Investors’ Beliefs and Cryptocurrency Prices

  The Review of Asset Pricing Studies · 2024 · 34 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Security
  • Monetary economics

  Abstract We explore the impact of investors’ beliefs on cryptocurrency demand and prices using new individual-level survey data and a structural characteristics-based demand model with differentiated cryptocurrencies and heterogeneous investors. We show that younger individuals with lower incomes are more optimistic about the future value of cryptocurrencies, as are late investors. We identify the model combining observable beliefs with an instrumental variable strategy that exploits variation in the production of different cryptocurrencies. Counterfactual analyses quantify the impact on portfolio allocations and equilibrium prices of (i) (regulating) entry of late optimistic investors, and (ii) growing concerns among investors about the sustainability of energy-intensive proof-of-work cryptocurrencies. (JEL: D84, G11, G41)

  PublisherDOI

• An Equilibrium Model of Rollover Lotteries

  SSRN Electronic Journal · 2024-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Replication Data for: Investors' Beliefs and Cryptocurrency Prices

  Harvard Dataverse · 2023-12-06

  datasetOpen accessSenior author

  Replication package for: Investors' Beliefs and Cryptocurrency Prices

  PublisherDOI

• When Cryptomining Comes to Town: High Electricity-Use Spillovers to the Local Economy

  SSRN Electronic Journal · 2023-01-01

  articleOpen access
  PublisherDOI

• When Cryptomining Comes to Town: High Electricity-use Spillovers to the Local Economy

  SSRN Electronic Journal · 2023-01-01 · 18 citations

  articleOpen access
  PublisherDOI

• Demand Estimation with Text and Image Data

  SSRN Electronic Journal · 2023-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Jason Abaluck

  Yale University

  12 shared

• Matteo Benetton

  11 shared

• Steven Berry

  8 shared

• Philip A. Haile

  5 shared

• Adair Morse

  5 shared

• Gregory Lewis

  University of Ontario Institute of Technology

  4 shared

• Marcelo Sant’Anna

  Faculdades Guarulhos

  3 shared

• Ilya Morozov

  Northwestern University

  2 shared

Awards & honors

• Distinguished Alumni Award