25 citations · 26 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…