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Takuya Takagi

10 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…

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
2022

Exploring the Whole Rashomon Set of Sparse Decision Trees

NeurIPS 2022accept

In any given machine learning problem, there may be many models that could explain the data almost equally well. However, most learning algorithms return only one of these models, leaving practitioners with no practical way to explore alternative models that might have desirable properties beyond wh…

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