Personalized Policy Learning through Discrete Experimentation: Theory and Empirical Evidence
通过离散实验学习个性化连续策略：理论与实证证据AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要利用离散处理水平的随机实验估计策略价值并学习个性化策略，直接对应因果推断与策略学习，未明确采用结构经济模型。
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通过离散实验学习个性化连续策略：理论与实证证据
- 研究问题
- 研究仅有有限离散处理水平的随机实验时，如何结合高维用户特征学习个性化连续决策，改善未测试水平的策略价值评估。
- 方法与思路
- 作者提出用于策略定向的深度学习框架 DLPT，将个性化策略价值估计与连续策略学习结合，并在社交平台内容创作激励中验证。
- 材料陈述的贡献
- 摘要称证明策略价值估计的渐近无偏性与一致性，并给出所学策略的 root-n 遗憾界；平台应用报告优于现有基准。
- 阅读关注点
- 应重点核对离散实验如何支持连续水平推断、插值或外推所需结构，以及遗憾界的定义、策略类限制和平台评估设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要未说明连续策略识别所需假设及效果数值，随机化本身不保证未测试水平可识别。
原文摘要
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.