4 citations · 4 across the 8 of their papers we have counts for
8 papers
Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables
Yoichi Chikahara
Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such as gender and race. Causal notions of…
Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data
Yoichi Chikahara
Heteroscedasticity -- where the variance of a variable changes with other variables -- is pervasive in real data, and elucidating why it arises from the perspective of statistical…
MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions
Hans Jarett Ong, Yoichi Chikahara, Tomoharu Iwata
Uncovering the causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interve…
Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
Yoichi Chikahara, Kansei Ushiyama
There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes. Achieving high performance in such hig…
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Shunsuke Horii, Yoichi Chikahara
Estimating heterogeneous treatment effects across individuals has attracted growing attention as a statistical tool for performing critical decision-making. We propose a Bayesian i…
Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
Tomoharu Iwata, Yoichi Chikahara
This article proposes a meta-learning method for estimating the conditional average treatment effect (CATE) from a few observational data. The proposed method learns how to estimat…
Feature Selection for Discovering Distributional Treatment Effect Modifiers
Yoichi Chikahara, Makoto Yamada, Hisashi Kashima
Finding the features relevant to the difference in treatment effects is essential to unveil the underlying causal mechanisms. Existing methods seek such features by measuring how g…
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino +1
Machine learning is used to make decisions for individuals in various fields, which require us to achieve good prediction accuracy while ensuring fairness with respect to sensitive…