Optimal Dynamic Treatment Regimes and Partial Welfare Ordering ︎︎︎
supplement ︎︎︎ matlab codes & data ︎︎︎ working paper ︎︎︎ slides ︎︎︎
Sep 2022,
Accepted,
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.
A Computational Approach to Identification of Treatment Effects for Policy Evaluation ︎︎︎
with Shenshen Yang
July 2023,
R&R, 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
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 ︎︎︎
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.
Semiparametric Models for Dynamic Treatment Effects and Mediation Analyses with Observational Data ︎︎︎
with Sungwon Lee
slides ︎︎︎