1 citations · 1 across the 8 of their papers we have counts for
9 papers
Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
Naoki Chihara, Tatsushi Oka, Yasuko Matsubara +2
We present a regression-adjustment framework designed for the estimation of longitudinal treatment effects in randomized experiments under static regimes. While regression-adjustme…
Rectified Linear Unit Regression
Tatsushi Oka
This paper develops a regression framework for analyzing integrated conditional distribution and quantile functions. The proposed method, termed rectified linear unit (ReLU) regres…
Distributional Treatment Effects of Content Promotion: Evidence from an ABEMA Field Experiment
Shota Yasui, Tatsushi Oka, Undral Byambadalai +1
We examine the impact of top-of-screen promotions on viewing time at ABEMA, a leading video streaming platform in Japan. To this end, we conduct a large-scale randomized controlled…
Beyond the Average: Distributional Causal Inference under Imperfect Compliance
Undral Byambadalai, Tomu Hirata, Tatsushi Oka +1
We study the estimation of distributional treatment effects in randomized experiments with imperfect compliance. When participants do not adhere to their assigned treatments, we le…
Latent Variable Modeling for Robust Causal Effect Estimation
Tetsuro Morimura, Tatsushi Oka, Yugo Suzuki +1
Latent variable models provide a powerful framework for incorporating and inferring unobserved factors in observational data. In causal inference, they help account for hidden fact…
Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks
Tomu Hirata, Undral Byambadalai, Tatsushi Oka +2
We propose a novel multi-task neural network approach for estimating distributional treatment effects (DTE) in randomized experiments. While DTE provides more granular insights int…
On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization
Undral Byambadalai, Tomu Hirata, Tatsushi Oka +1
This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Ef…
Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction
Undral Byambadalai, Tatsushi Oka, Shota Yasui
We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been exten…
Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials
Tatsushi Oka, Shota Yasui, Yuta Hayakawa +1
In this paper, we address the issue of estimating and inferring distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comp…