3 papers
cs.LG2026
Backward Compatibility in Tree-Based Explanations and Enhanced CART Algorithm
Hirofumi Suzuki
In the operation of machine learning models, model update is a fundamental process that requires careful consideration of its impact on downstream decision-making. Particularly whe…
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.ML2026
Sparse Additive Model Pruning for Order-Based Causal Structure Learning
Kentaro Kanamori, Hirofumi Suzuki, Takuya Takagi
Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observati…