Search Results for author: Takashi Nicholas Maeda

Found 3 papers, 0 papers with code

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

no code implementations14 Jan 2024 Takashi Nicholas Maeda, Shohei Shimizu

Moreover, by incorporating the prior knowledge that causes precedes their effects in time, we extend the first algorithm to the second method for causal discovery in time series data.

Additive models Causal Discovery +1

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

no code implementations13 Jan 2020 Takashi Nicholas Maeda, Shohei Shimizu

The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables.

Causal Discovery

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