4 papers
Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
Mariyam Khan, Shohei Shimizu, Thong Pham
We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM). Existing methods that…
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…
Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
Tadahisa Okuda, Shohei Shimizu, Thong Pham +2
Causal discovery has achieved substantial theoretical progress, yet its deployment in large-scale longitudinal systems remains limited. A key obstacle is that operational data are…
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…