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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.LG2025
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…