5 papers
Estimating heterogeneous treatment effects by W-MCM based on Robust reduced rank regression
Ryoma Hieda, Shintaro Yuki, Kensuke Tanioka +1
Recently, from the personalized medicine perspective, there has been an increased demand to identify subgroups of subjects for whom treatment is effective. Consequently, the estima…
Extension of W-method and A-learner for multiple binary outcomes
Shintaro Yuki, Kensuke Tanioka, Hiroshi Yadohisa
In this study, we compared two groups, in which subjects were assigned to either the treatment or the control group. In such trials, if the efficacy of the treatment cannot be demo…
Bayesian Geographically Weighted Regression using Fused Lasso Prior
Toshiki Sakai, Jun Tsuchida, Hiroshi Yadohisa
A main purpose of spatial data analysis is to predict the objective variable for the unobserved locations. Although Geographically Weighted Regression (GWR) is often used for this…
Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects
Takehiro Shoji, Jun Tsuchida, Hiroshi Yadohisa
When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of co…
Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of main effects
Mayu Hiraishi, Ke Wan, Kensuke Tanioka +2
This study proposes a novel framework based on the RuleFit method to estimate Heterogeneous Treatment Effect (HTE) in a randomized clinical trial. To achieve this, we adopted S-lea…