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Takashi Nicholas Maeda

3 accepted papers

2026

I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables

AAAI 2026technical

Causal discovery from observational data is a fundamental tool in various fields of science. While existing approaches are typically designed for a single dataset, we often need to handle multiple datasets with non-identical variable sets in practice. One straightforward approach is to estimate a ca

Cited by 0SourcePDFScholar
2020

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

AISTATS 2020poster

Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper…

Cited by 48SourcePDFScholar