no code implementations • 20 Mar 2024 • Takuya Ura, Lina Zhang
This paper provides a framework for the policy relevant treatment effects using instrumental variables.
no code implementations • 18 Apr 2023 • Yukun Ma, Pedro H. C. Sant'Anna, Yuya Sasaki, Takuya Ura
In this paper, we derive a new class of doubly robust estimators for treatment effect estimands that is also robust against weak covariate overlap.
no code implementations • 5 Apr 2023 • Jackson Bunting, Takuya Ura
Our proposed estimator uses the equality constraints to decrease the dimension of the optimization problem, thereby generating computational gains.
no code implementations • 22 Oct 2021 • Yuya Sasaki, Takuya Ura
Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations).
no code implementations • 12 Feb 2021 • Harold D. Chiang, Kengo Kato, Yuya Sasaki, Takuya Ura
We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints.
no code implementations • 14 Dec 2020 • Yuya Sasaki, Takuya Ura
Consider a causal structure with endogeneity (i. e., unobserved confoundedness) in empirical data, where an instrumental variable is available.
no code implementations • 5 Oct 2020 • Federico A. Bugni, Jackson Bunting, Takuya Ura
We refer to this as the "homogeneity assumption" in dynamic discrete games.
no code implementations • 27 Jul 2020 • Yuya Sasaki, Takuya Ura, Yichong Zhang
This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data.