We consider off-policy evaluation in the contextual bandit setting for the purpose of obtaining a robust off-policy selection strategy, where the selection strategy is evaluated based on the value of the chosen policy in a set of proposal (target) policies. We propose a new method to compute a lower bound on the value of an arbitrary target policy given some logged data in contextual bandits for a desired coverage. The lower bound is built around the so-called Self-normalized Importance Weighting (SN) estimator. It combines the use of a semi-empirical Efron-Stein tail inequality to control the concentration and Harris' inequality to control the bias. The new approach is evaluated on a number of synthetic and real datasets and is found to be superior to its main competitors, both in terms of tightness of the confidence intervals and the quality of the policies chosen.

该研究考虑在上下文匹配机器学习算法的偏离策略评估中，提出了一种新的方法——基于自归一化重要性权重估算目标策略的价值下界，并在合成和实际数据集上测试表明该方法可获得更优越的策略，包括更紧密的置信区间和选择的质量。

通过自标准化的重要性权重实现自信的离线评估和选择