{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/state-relevance-for-off-policy-evaluation","title":"State Relevance for Off-Policy Evaluation","arxiv_id":"2109.06310","date":"2021-09-13","proceeding":null,"authors":["Simon P. Shen","Yecheng Jason Ma","Omer Gottesman","Finale Doshi-Velez"],"abstract":"Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions. However, the variance of these estimators is often high, especially when trajectories are of different lengths. 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