Sep 2022,
Forthcoming,
Journal of American Statistical Association

*Editor’s Choice 2023

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.



Dec 2023,

Forthcoming,
Journal of Econometrics


We propose a computational framework to calculate sharp nonparametric bounds (using binary IV that satisfies full independence) 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


arxiv ︎︎︎ slides ︎︎︎

Nov 2022


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 magazine ︎︎︎ and included in the MIT graduate machine learning course ︎︎︎.



Jan 2024

Most work on treatment choice and policy learning focuses on utilitarian welfare (i.e., average welfare), which can be sensitive to skewed heterogeneity. We propose a robust policy learning framework that enables the policymaker to act with prudence/negligence and to be influenced by vote shares.



Aug 2023

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.


© Sukjin Han