7 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…
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…
Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
Mingqian Guan, Komei Fujita, Naoya Sueishi +1
This paper proposes a new method for estimating conditional average treatment effects (CATE) in randomized experiments. We adopt inverse probability weighting (IPW) for identificat…
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…