Offline Multi-Action Policy Learning: Generalization and Optimization
离线多行动策略学习：泛化与优化AI 解读 ↓
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离线多行动策略学习：泛化与优化
- 研究问题
- 如何利用观测数据学习由个体特征映射到多个行动的策略，并在预算约束或决策树等受限策略类中控制决策遗憾？
- 方法与思路
- 作者基于有效半参数推断构造策略学习算法，并针对决策树实现提出混合整数规划与树搜索两条计算路径。
- 材料陈述的贡献
- 摘要称，算法达到渐近极小极大最优遗憾，并报告相较既有算法的性能改善；决策树优化方案进一步回应实施中的计算挑战。
- 阅读关注点
- 应核对观测数据下策略价值的识别条件、遗憾定义与最优性范围，以及预算约束和两类树优化算法的求解保证。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明完整识别条件、比较数值，首创与性能改善均属作者陈述。
原文摘要
In many settings, a decision-maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action. Examples include selecting offers, prices, advertisements, or emails to send to consumers, as well as the problem of determining which medication to prescribe to a patient. While there is a growing body of literature devoted to this problem, most existing results are focused on the case where data comes from a randomized experiment, and further, there are only two possible actions, such as giving a drug to a patient or not. In this paper, we study the offline multi-action policy learning problem with observational data and where the policy may need to respect budget constraints or belong to a restricted policy class such as decision trees. We build on the theory of efficient semi-parametric inference in order to propose and implement a policy learning algorithm that achieves asymptotically minimax-optimal regret. To the best of our knowledge, this is the first result of this type in the multi-action setup, and it provides a substantial performance improvement over the existing learning algorithms. We then consider additional computational challenges that arise in implementing our method for the case where the policy is restricted to take the form of a decision tree. We propose two different approaches, one using a mixed integer program formulation and the other using a tree-search based algorithm.