2 citations · 3 across the 3 of their papers we have counts for
6 papers
Efficient difference-in-differences estimation under partial interference with incremental propensity score policies
Junjie Li, Yukitoshi Matsushita
This paper develops efficient difference-in-differences (DID) estimation under partial interference with a cluster incremental propensity score (CIPS) policy. We define direct and…
Empirical Likelihood for Random Forests and Ensembles
Harold D. Chiang, Yukitoshi Matsushita, Taisuke Otsu
We develop an empirical likelihood (EL) framework for random forests and related ensemble methods, providing a likelihood-based approach to quantify their statistical uncertainty.…
A difference-in-differences estimator by covariate balancing propensity score
Junjie Li, Yukitoshi Matsushita
This article develops a covariate balancing approach for the estimation of treatment effects on the treated (ATT) in a difference-in-differences (DID) research design when panel da…
Regression adjustment in completely randomized experiments with many covariates
Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
This paper investigates estimation and inference for average treatment effects in completely randomized experiments when researchers observe potentially many covariates. Within Ney…
Conditional Likelihood Ratio Test with Many Weak Instruments
Sreevidya Ayyar, Yukitoshi Matsushita, Taisuke Otsu
This paper extends validity of the conditional likelihood ratio (CLR) test developed by Moreira (2003) to instrumental variable regression models with unknown error variance and ma…
Multiway empirical likelihood
Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
This paper develops a general methodology to conduct statistical inference for observations indexed by multiple sets of entities. We propose a novel multiway empirical likelihood s…