no code implementations • 20 Jul 2023 • Masahiro Kato, Akari Ohda, Masaaki Imaizumi, Kenichiro McAlinn
In this paper, we first point out that existing SCMs suffer from an implicit endogeneity problem, which is the correlation between the outcomes of untreated units and the error term in the model of a counterfactual outcome.
no code implementations • 10 Mar 2022 • Danielle Cabel, Shonosuke Sugasawa, Masahiro Kato, Kosaku Takanashi, Kenichiro McAlinn
Spatial data are characterized by their spatial dependence, which is often complex, non-linear, and difficult to capture with a single model.
no code implementations • NeurIPS 2021 • Masahiro Kato, Kenichiro McAlinn, Shota Yasui
This paper proposes a DR estimator for dependent samples obtained from adaptive experiments.
no code implementations • ICLR 2022 • Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, Haruo Kakehi
We consider learning causal relationships under conditional moment restrictions.
no code implementations • 16 Sep 2021 • Kaito Ariu, Masahiro Kato, Junpei Komiyama, Kenichiro McAlinn, Chao Qin
We consider the "policy choice" problem -- otherwise known as best arm identification in the bandit literature -- proposed by Kasy and Sautmann (2021) for adaptive experimental design.
no code implementations • 3 Aug 2021 • Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Haruo Kakehi, Shota Yasui
To address this issue, we propose a method that transforms conditional moment restrictions to unconditional moment restrictions through importance weighting, using a conditional density ratio estimator.
no code implementations • 11 Mar 2021 • Kenichiro McAlinn, Kosaku Takanashi
In this regard, Fernandez-Villaverde, Rubio-Ramirez, and Santos (2006) show convergence of the likelihood, when the shock has compact support.
no code implementations • 15 Feb 2021 • Junpei Komiyama, Masaya Abe, Kei Nakagawa, Kenichiro McAlinn
We achieve superior statistical power to existing methods and prove that the false discovery rate is controlled.
no code implementations • 8 Oct 2020 • Masahiro Kato, Shota Yasui, Kenichiro McAlinn
This paper proposes a DR estimator for dependent samples obtained from adaptive experiments.
no code implementations • 20 Nov 2019 • Kōsaku Takanashi, Kenichiro McAlinn
To analyze the theoretical predictive properties of statistical methods under this setting, we first define the Kullback-Leibler risk, in order to place the problem within a decision theoretic framework.