6 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…
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…
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…
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
Masayuki Takayama, Tadahisa Okuda, Thong Pham +4
In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is important for reasonable causal models reflecting the broad…
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…