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1/Off-policy evaluation is required when we want to use data logged with a different policy e.g. advertising, drug trials. But there is covariate shift and we need to adjust for it. We propose robust methods #AI @anqi_liu33 @2039648937Hao @yisongyuehttps://twitter.com/arXiv_Daily/status/1195343951340826631 …
2/Ours is triply robust because it is robust in accounting for covariate shifts in data. We combine this robustness with doubly robust estimator, which combine two estimators, and guarantees unbiased estimation if either estimator is correct. Hence, the name triply robust!
3/Benefit of triply robust algorithm is superior practical performance + theoretical guarantees in terms of bias + variance bounds, as well as minimax bounds. Below orange is our method. In most cases error is reduced significantly.pic.twitter.com/vKryNWiurh
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