4 papers
Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
Mingqian Guan, Komei Fujita, Naoya Sueishi +1
This paper proposes a new method for estimating conditional average treatment effects (CATE) in randomized experiments. We adopt inverse probability weighting (IPW) for identificat…
On the Informativeness of Specification Tests for Estimator Validity
Naoya Sueishi
Empirical researchers often use model specification tests, such as Hausman tests and overidentifying restrictions tests, to assess the validity of estimators rather than the correc…
Trigonometrically approximated maximum likelihood estimation for stable law
Muneya Matsui, Naoya Sueishi
A trigonometrically approximated maximum likelihood estimation for -stable laws is proposed. The estimator solves the approximated likelihood equation, which is obtained by proj…
Small Tuning Parameter Selection for the Debiased Lasso
Akira Shinkyu, Naoya Sueishi
In this study, we investigate the bias and variance properties of the debiased Lasso in linear regression when the tuning parameter of the node-wise Lasso is selected to be smaller…