Research

    Publications:

1.  csa2sls: A Complete  Subset Approach for Many  Instruments using Stata (with Seojeong Lee, Siha Lee and Youngki Shin). 

We develop a Stata command csa2sls that implements the complete subset averaging two-stage least squares (CSA2SLS) estimator in Lee and Shin (2021). The CSA2SLS estimator is an alternative to the two-stage least squares estimator that remedies the bias issue caused by many correlated instruments. We conduct Monte Carlo simulations and confirm that the CSA2SLS estimator reduces both the mean squared error and the estimation bias substantially when instruments are correlated. We illustrate the usage of csa2sls in Stata by an empirical application. [arXiv]  Stata Journal, Forthcoming, 2023+

      Working Papers:

1.  A Nonparametric Test of Heterogeneous Treatment Effects Under Interference.

Statistical inference of heterogeneous treatment effects (HTEs) is confounded when  economic units interact because treatment effects may vary by pre-treatment variables, post-treatment variables (that measure the exposure to other units’ treatment statuses), or both. It invalidates the standard hypothesis testing techniques used to infer HTEs. In this paper, I develop statistical inference  methods  to detect HTEs and disentangle the drivers of treatment effects heterogeneity in populations where units interact. Specifically, I incorporate clustered interaction into the potential outcomes model and propose kernel-based test statistics for the null hypotheses of (a) no HTEs by treatment assignment (or post-treatment exposure variables) for all pre-treatment variables values; and (b) no HTEs by pre-treatment variables for all treatment assignment vectors. To disentangle the source of heterogeneity in treatment effects, I recommend a multiple-testing algorithm.  I prove the asymptotic properties of the proposed test statistics via a  modern poissonization technique. Furthermore, I propose bootstrap methods that better approximate the null distributions in finite-sample settings. Monte Carlo simulation evidence corroborates the theoretical findings in the paper. Finally, to gain practical insight, I illustrate the usage of the proposed tests in an empirical application using an experimental data set from a Chinese weather insurance program.  [Paper, Summary] 

2.  Statistical Treatment Rules under Social Interaction  (with Seungjin Han and Young ki Shin).

In this paper we study treatment assignment rules in the presence of social interaction. We construct an analytical framework under the anonymous interaction assumption, where the decision problem becomes choosing a treatment fraction. We propose a multinomial empirical success (MES) rule that includes the empirical success rule of Manski (2004) as a special case. We investigate the non-asymptotic bounds of the expected utility based on the MES rule. Finally, we prove that the MES rule achieves the asymptotic optimality with the minimax regret criterion. [arXiv, Replication Code] Under review, November 2022  

3. Randomization Inference of Heterogeneous Treatment Effects Under Interference.

We design randomization tests of heterogeneous treatment effects when units interact on a network. Our modeling strategy allows network interference into the potential outcomes framework using the concept of network exposure mapping. We consider three null hypotheses that represent different notions of homogeneous treatment effects, but due to nuisance parameters and the multiplicity of potential outcomes, the hypotheses are not sharp. To address the issue of multiple potential outcomes, we propose a conditional randomization inference  method that expands on existing methods. Additionally, we propose two techniques that overcome the nuisance parameter issue. We show that our conditional randomization inference method, combined with either of the proposed techniques for handling nuisance parameters, produces asymptotically valid p-values. [Paper]