3 papers
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…
stat.ME2026
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…
stat.ML2024
Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
Hiroshi Yokoyama, Ryusei Shingaki, Kaneharu Nishino +2
Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain "black boxes", making prediction error diagnosis chall…