5 papers · 1 filter
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…
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…