5 papers
AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings
Shun Yanashima, Kentaro Kanamori, Hirofumi Suzuki
Causal discovery becomes particularly challenging when the available sample size is small relative to the number of variables. This challenge also arises in the linear non-Gaussian…
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…
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…
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…
Rule Mining for Correcting Classification Models
Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi +2
Machine learning models need to be continually updated or corrected to ensure that the prediction accuracy remains consistently high. In this study, we consider scenarios where dev…