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Kentaro Kanamori

8 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
2024

Learning Decision Trees and Forests with Algorithmic Recourse

ICML 2024spotlight

This paper proposes a new algorithm for learning accurate tree-based models while ensuring the existence of recourse actions. Algorithmic Recourse (AR) aims to provide a recourse action for altering the undesired prediction result given by a model. Typical AR methods provide a reasonable action by s…

2022

Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees

AISTATS 2022poster

Counterfactual Explanation (CE) is a post-hoc explanation method that provides a perturbation for altering the prediction result of a classifier. An individual can interpret the perturbation as an "action" to obtain the desired decision results. Existing CE methods focus on providing an action, whic…

2021

Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization

AAAI 2021technical

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result.…

2020

DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization

IJCAI 2020poster

Counterfactual Explanation (CE) is one of the post-hoc explanation methods that provides a perturbation vector so as to alter the prediction result obtained from a classifier. Users can directly interpret the perturbation as an "action" for obtaining their desired decision results. However, an actio…

Cited by 0SourcePDFScholar