Counterfactual estimators are critical for learning and refining policies using logged data, a process known as Off-Policy Evaluation (OPE). OPE allows researchers to assess new policies without costly experiments, speeding up the evaluation process. Online experimental methods, such as A/B tests, are effective but often slow, thus delaying the policy selection and optimization process. In this work, we explore the application of OPE methods in the context of resource allocation in dynamic auction environments. Given the competitive nature of environments where rapid decision-making is crucial for gaining a competitive edge, the ability to quickly and accurately assess algorithmic performance is essential. By utilizing counterfactual estimators as a preliminary step before conducting A/B tests, we aim to streamline the evaluation process, reduce the time and resources required for experimentation, and enhance confidence in the chosen policies. Our investigation focuses on the feasibility and effectiveness of using these estimators to predict the outcomes of potential resource allocation strategies, evaluate their performance, and facilitate more informed decision-making in policy selection. Motivated by the outcomes of our initial study, we envision an advanced analytics system designed to seamlessly and dynamically assess new resource allocation strategies and policies.

本研究解决了在动态拍卖环境中快速有效评估资源分配策略的需求问题。通过将反事实估计器与传统的A/B测试相结合，提出了一种新颖的方法来简化评估过程，并提高政策选择的效率和准确性。研究表明，这种方法能够减少实验所需的时间和资源，从而提升决策的信心与效果。

动态拍卖环境中的离政策评估与反事实方法