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Hirofumi Suzuki

3 accepted papers

2026

I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables

AAAI 2026technical

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 handle multiple datasets with non-identical variable sets in practice. One straightforward approach is to estimate a ca

Cited by 0SourcePDFScholar
2026

Sparse Additive Model Pruning for Order-Based Causal Structure Learning

AAAI 2026technical

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 observational data. One of the major frameworks is the order-based approach that first estimates a topological order of the under

Cited by 0SourcePDFScholar
2022

Explainable and Local Correction of Classification Models Using Decision Trees

AAAI 2022technical

In practical machine learning, models are frequently updated, or corrected, to adapt to new datasets. In this study, we pose two challenges to model correction. First, the effects of corrections to the end-users need to be described explicitly, similar to standard software where the corrections are…

Cited by 2SourcePDFScholar