Sep 2022,
Accepted with minor revisions,
Journal of American Statistical Association


We partially identify the optimal dynamic regime from observational data, relaxing sequential randomization but instead using IVs. As a first step, we establish the sharp partial ordering of welfares, which summarizes the signs of dynamic treatment effects.



Apr 2022,

R&R, Journal of Econometrics


We propose a framework to systematically calculate sharp nonparametric bounds (using binary IV) on various policy-relevant treatment parameters that are defined as weighted averages of the MTE.


Shapes as Product Differentiation ︎︎︎


with Eric Schulman, Kristen Grauman, Santhosh Ramakrishnan


slides ︎︎︎

Nov 2022

Submitted


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 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 by using the embeddings in a synthetic control method.

*This project is featured in a typography magazine ︎︎︎ and included in the MIT graduate machine learning course ︎︎︎.




On Quantile Treatment Effects, Rank Similarity, and the Variation of IVs 


with Haiqing Xu


slides ︎︎︎

Oct 2022

New draft coming soon!

We provide a novel interpretation for rank similarity, which motivates us to relax it in a way that is useful to bound the QTEs using multi-valued IVs. The bounds are easy to calculate in practice and are shown to be informative.


Testing for Information Structure under Bayes Correlated Equilibrium


with Hiro Kaido, Lorenzo Magnolfi, Camilla Roncoroni


in progress


© Sukjin Han