no code implementations • 14 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.
no code implementations • 4 Jun 2021 • Takashi Nicholas Maeda, Shohei Shimizu
In this study, we focus on causal additive models in the presence of unobserved variables.
no code implementations • 13 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.