55 citations · 208 across the 25 of their papers we have counts for
8 papers · 1 filter
Causal-learn: Causal Discovery in Python
Yujia Zheng, Biwei Huang, Wei Chen +6
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe , an open-so…
Discovery of Causal Additive Models in the Presence of Unobserved Variables
Takashi Nicholas Maeda, Shohei Shimizu
Causal discovery from data affected by unobserved variables is an important but difficult problem to solve. The effects that unobserved variables have on the relationships between…
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Yan Zeng, Shohei Shimizu, Ruichu Cai +3
Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the si…
Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
Takashi Nicholas Maeda, Shohei Shimizu
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…
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data
Chao Li, Shohei Shimizu
Estimating causal models from observational data is a crucial task in data analysis. For continuous-valued data, Shimizu et al. have proposed a linear acyclic non-Gaussian model to…
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Shohei Shimizu, Aapo Hyvarinen, Yoshinobu Kawahara
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typic…