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.