25 citations · 64 across the 19 of their papers we have counts for
20 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…
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…
Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data
Masahiro Kato, Shuting Wu, Kodai Kureishi +1
We address the issue of binary classification from positive and unlabeled data (PU classification) with a selection bias in the positive data. During the observation process, (i) a…
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn +2
We consider learning causal relationships under conditional moment restrictions. Unlike causal inference under unconditional moment restrictions, conditional moment restrictions po…
A Practical Guide of Off-Policy Evaluation for Bandit Problems
Masahiro Kato, Kenshi Abe, Kaito Ariu +1
Off-policy evaluation (OPE) is the problem of estimating the value of a target policy from samples obtained via different policies. Recently, applying OPE methods for bandit proble…
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy
Masahiro Kato, Shota Yasui, Kenichiro McAlinn
The doubly robust (DR) estimator, which consists of two nuisance parameters, the conditional mean outcome and the logging policy (the probability of choosing an action), is crucial…
Learning Classifiers under Delayed Feedback with a Time Window Assumption
Masahiro Kato, Shota Yasui
We consider training a binary classifier under delayed feedback (\emph{DF learning}). For example, in the conversion prediction in online ads, we initially receive negative samples…
A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
Shota Yasui, Gota Morishita, Komei Fujita +1
In display advertising, predicting the conversion rate, that is, the probability that a user takes a predefined action on an advertiser's website, such as purchasing goods is funda…
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
Masahiro Kato, Masatoshi Uehara, Shota Yasui
We consider evaluating and training a new policy for the evaluation data by using the historical data obtained from a different policy. The goal of off-policy evaluation (OPE) is t…
Debiased Off-Policy Evaluation for Recommendation Systems
Yusuke Narita, Shota Yasui, Kohei Yata
Efficient methods to evaluate new algorithms are critical for improving interactive bandit and reinforcement learning systems such as recommendation systems. A/B tests are reliable…
Dual Learning Algorithm for Delayed Conversions
Yuta Saito, Gota Morishita, Shota Yasui
In display advertising, predicting the conversion rate (CVR), meaning the probability that a user takes a predefined action on an advertiser's website, is a fundamental task for es…
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Yuta Saito, Shota Yasui
We study the model selection problem in conditional average treatment effect (CATE) prediction. Unlike previous works on this topic, we focus on preserving the rank order of the pe…
Fatigue-Aware Ad Creative Selection
Daisuke Moriwaki, Komei Fujita, Shota Yasui +1
In online display advertising, selecting the most effective ad creative (ad image) for each impression is a crucial task for DSPs (Demand-Side Platforms) to fulfill their goals (cl…
Efficient Counterfactual Learning from Bandit Feedback
Yusuke Narita, Shota Yasui, Kohei Yata
What is the most statistically efficient way to do off-policy evaluation and optimization with batch data from bandit feedback? For log data generated by contextual bandit algorith…