Our paper entitled "Feature Selection for Discovering Distributional Treatment Effect Modifiers" has been accepted to
#UAI2022
‼️🎊🙌😆🤖🎉🎉 Stay tuned for our arXiv preprint 📝🕺💫
My research grant proposal entitled "Causal Inference from Incomplete Data for Fair Machine Learning Prediction" has been accepted to Japan Science and Technology Agency (ACT-X) 🤖😃🤖
Our paper entitled "Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint" has been accepted to
#AISTATS2021
🎉🎉🙌😆🆗 Preprint is available here
Our article entitled "Meta-learning for heterogeneous treatment effect estimation with closed-form solvers" has been accepted to Machine Learning journal 😃 Congrats, Tomoharu! 👏👶
We tackled few-shot CATE estimation problem via meta-learning. Preprint:
Our paper entitled "Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation" has been accepted to
#UAI2024
!! 🎊🙌😃🎉
This was a summer internship project with a great Ph.D candidate, Kansei 🏝️🤖🏄♂️ Stay tuned for details!😄
We are thrilled to my co-authored paper titled “Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model” has been accepted to
#AAAI2024
🎉🎉🎉 Congrats Shunsuke! (
@holyshun
) 👏😃
Our journal article entitled "Making Individually Fair Predictions with Causal Pathways" has been accepted for publication in Special issue on "Bias and Fairness in AI" of Data Mining and Knowledge Discovery (DAMI) ‼️🙌😋🤖🤝🌈🎉🎉
Are you interested in explaining why the treatment effects are different across individuals?🤔 Check out our
#UAI2022
paper💡, which selects the feature attributes related to the degree of "distributional" treatment effects. Now our preprint is avaiable📝: