Mar 2022,
R&R, 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
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: Neural Network Embedding in the Analysis of Markets for Fonts ︎︎︎
with Eric Schulman, Kristen Grauman, Santhosh Ramakrishnan
Nov 2021
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 ︎︎︎.
