通过离散实验实现个性化政策学习：理论与实证证据
Personalized Policy Learning through Discrete Experimentation: Theory and Empirical Evidence
AI总结：
本文提出DLPT框架，通过高维用户特征学习个性化连续政策，实证验证在优化内容创作激励水平上优于现有方法。
摘要：
随机对照试验（RCTs），或A/B测试，已成为优化在线平台各种运营政策的黄金标准。然而，这些平台上的RCTs通常仅涵盖有限数量的离散处理水平，而平台日益面临的复杂运营挑战涉及优化连续变量，如定价和激励计划。当前行业实践是将这些连续决策变量离散化为几个处理水平，并选择最优的离散处理水平。然而，这种方法往往导致次优决策，因为它无法准确外推未测试处理水平的性能，并且未能考虑用户特征在处理效果上的异质性。本研究通过开发一个理论严谨且经实证验证的框架来学习基于高维用户特征的个性化连续政策，利用仅包含离散处理水平的RCT观察数据。具体而言，我们引入了深度学习用于政策定向（DLPT）框架，包括个性化政策价值估计和个性化政策学习。我们证明我们的政策价值估计器是渐近无偏且一致的，所学习的政策实现了根-n-后悔界。我们与一家领先的社会媒体平台合作，实证验证了我们的方法以优化内容创作的激励水平。结果表明，我们的DLPT框架显著优于现有基准，实现了在评估每个用户群体政策价值和识别最优个性化政策方面的实质性改进。
英文摘要：
Randomized Controlled Trials (RCTs), or A/B testing, have become the gold standard for optimizing various operational policies on online platforms. However, RCTs on these platforms typically cover a limited number of discrete treatment levels, while the platforms increasingly face complex operational challenges involving optimizing continuous variables, such as pricing and incentive programs. The current industry practice involves discretizing these continuous decision variables into several treatment levels and selecting the optimal discrete treatment level. This approach, however, often leads to suboptimal decisions as it cannot accurately extrapolate performance for untested treatment levels and fails to account for heterogeneity in treatment effects across user characteristics. This study addresses these limitations by developing a theoretically solid and empirically verified framework to learn personalized continuous policies based on high-dimensional user characteristics, using observations from an RCT with only a discrete set of treatment levels. Specifically, we introduce a deep learning for policy targeting (DLPT) framework that includes both personalized policy value estimation and personalized policy learning. We prove that our policy value estimators are asymptotically unbiased and consistent, and the learned policy achieves a root-n-regret bound. We empirically validate our methods in collaboration with a leading social media platform to optimize incentive levels for content creation. Results demonstrate that our DLPT framework significantly outperforms existing benchmarks, achieving substantial improvements in both evaluating the value of policies for each user group and identifying the optimal personalized policy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
