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Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus …
Yuta Saito
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Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
We study the model selection problem in mph{conditional average treatment effect} (CATE) prediction. Unlike previous works on this …
Yuta Saito, Shota Yasui
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Dual Learning Algorithm for Delayed Conversions
In display advertising, predicting the conversion rate (CVR), meaning the probability that a user takes a predefined action on an …
Yuta Saito, Gota Morishita, Shota Yasui
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Cost-Effective and Stable Policy Optimization Algorithm for Uplift Modeling with Multiple Treatments
Uplift modeling aims to optimize treatment policies and is a promising method for causal-based personalization in various domains such …
Yuta Saito, Hayato Sakata, Kazuhide Nakata
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Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of …
Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata
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Doubly Robust Prediction and Evaluation Methods Improve Uplift Modeling for Observational Data
Uplift modeling aims to optimize treatment allocation by predicting the net effect of a treatment on each individual (ITE) and is …
Yuta Saito, Hayato Sakata, Kazuhide Nakata
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