2 citations · 2 across the 6 of their papers we have counts for
6 papers
I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables
Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi +3
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…
Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data
Takashi Nicholas Maeda, Shohei Shimizu, Hidetoshi Matsui
We address the problem of inferring the causal direction between a continuous variable and a discrete variable from observational data. For the model , we adopt th…
Causal Additive Models with Unobserved Causal Paths and Backdoor Paths
Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between…
Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data
Takashi Nicholas Maeda, Shohei Shimizu
This paper proposes two methods for causal additive models with unobserved variables (CAM-UV). CAM-UV assumes that the causal functions take the form of generalized additive models…
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 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…