Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments︎︎︎
with Victor Chernozhukov, Iván Fernández-Val, Kaspar Wüthrich
arxiv︎︎︎ slides︎︎︎
Sep 2026
Revise & Resubmit
Econometrica
We identify average and quantile treatment effects for binary, ordered and continuous treatments with only binary IV under local copula invariance. The resulting semiparametric estimation procedures are very easy to implement.
Sep 2026
Revise & Resubmit
Journal of Econometrics
Finding IVs is a heuristic and creative process, and justifying exclusion restrictions is largely rhetorical. We propose using LLMs to search for new IVs systematically through narratives and counterfactual reasoning. We develop a discovery pipeline (multi-step, role-playing prompts) and an evaluation pipeline (expert surveys and an LLM judge trained on the survey data).
*This project is featured in the causal inference course at Stanford︎︎︎ and the Lindau Nobel Laureate Meetings 2025︎︎︎.
*This project is featured in the causal inference course at Stanford︎︎︎ and the Lindau Nobel Laureate Meetings 2025︎︎︎.
Testing Information Ordering for Strategic Agents︎︎︎
with Hiro Kaido, Lorenzo Magnolfi
arxiv︎︎︎ slides︎︎︎
Jan 2026
Revise & Resubmit
Journal of Econometrics
We develop a test of information ordering to examine if the true information structure is at least as informative as a proposed baseline. We utilize the notion of Bayes Correlated Equilibrium (BCE).
Mar 2025
Revise & Resubmit
Quantitative Economics
We develop new inference tools for parameters whose data-driven selection can be characterized as a polyhedral event. Examples include welfare evaluated at policies selected via minimax-regret and maximin-welfare rules.
Shapes as Product Differentiation︎︎︎
with Eric Schulman, Kristen Grauman, Santhosh Ramakrishnan
arxiv︎︎︎ slides︎︎︎
Nov 2022
Revise & Resubmit
RAND Journal of Economics
Many differentiated products have key attributes that are high-dimensional (e.g., design, text). We consider one of the simplest design products, fonts, and quantify their shapes by constructing neural network embeddings. Using the embeddings and data from the world's largest online market place for fonts, we study the causal effect of a merger on the merging firm's creative decisions of product differentiation.
*This project is featured in a typography magazine︎︎︎ and included in the MIT graduate machine learning course︎︎︎.
