Takashi Nicholas MAEDA

Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data.

Takashi Nicholas Maeda, Shohei Shimizu

CoRR, abs/2401.07231, 2024

Python package for causal discovery based on LiNGAM

Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, and Shohei Shimizu

Journal of Machine Learning Research, 24(14), Jan, 2023 Peer-reviewed

The economic value of urban landscapes in a suburban city of Tokyo, Japan: A semantic segmentation approach using Google Street View images

Masatomo Suzuki, Junichiro Mori, Takashi Nicholas Maeda, Jun Ikeda

Journal of Asian Architecture and Building Engineering, 22(3) 1110-1125, Jul 14, 2022 Peer-reviewed

I-RCD: An improved algorithm of repetitive causal discovery from data with latent confounders

Takashi Nicholas MAEDA

Behaviormetrika, Jun 4, 2022 Peer-reviewedInvitedLead authorLast authorCorresponding author

Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders

Takashi Nicholas MAEDA, Shohei SHIMIZU

International Journal of Data Science and Analytics, 13(2) 77-89, Sep 7, 2021 Peer-reviewedLead author

Causal Additive Models with Unobserved Variables

Takashi Nicholas MAEDA, Shohei SHIMIZU

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 161 97-106, Jul 27, 2021 Peer-reviewed

Discovery of Causal Additive Models in the Presence of Unobserved Variables.

Takashi Nicholas Maeda, Shohei Shimizu

CoRR, abs/2106.02234, 2021

RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders

T. N. Maeda, S. Shimizu

JMLR Workshop and Conference Proceedings, AISTATS2020 (Proc. 23rd International Conference on Artificial Intelligence and Statistics), 735-745, May, 2020 Peer-reviewed

Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders.

Takashi Nicholas Maeda, Shohei Shimizu

CoRR, abs/2001.04197, 2020

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