Long-term Off-Policy Evaluation and Learning

Author:

Saito Yuta1ORCID,Abdollahpouri Himan2ORCID,Anderton Jesse2ORCID,Carterette Ben2ORCID,Lalmas Mounia3ORCID

Affiliation:

1. Cornell University, Ithaca, NY, USA

2. Spotify, New York, NY, USA

3. Spotify, London, United Kingdom

Publisher

ACM

Reference43 articles.

1. Aman Agarwal, Soumya Basu, Tobias Schnabel, and Thorsten Joachims. 2017. Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers. KDD (2017), 687--696.

2. Imad Aouali, Victor-Emmanuel Brunel, David Rohde, and Anna Korba. 2023. Exponential Smoothing for Off-Policy Learning. arXiv preprint arXiv:2305.15877 (2023).

3. Susan Athey, Raj Chetty, and Guido Imbens. 2020. Combining experimental and observational data to estimate treatment effects on long term outcomes. arXiv preprint arXiv:2006.09676 (2020).

4. Using Survival Models to Estimate User Engagement in Online Experiments

5. Jiafeng Chen and David M Ritzwoller. 2021. Semiparametric estimation of long-term treatment effects. arXiv preprint arXiv:2107.14405 (2021).

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