3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…