Learning treatment effects while treating those in need
在帮助有需要者的同时学习处理效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究通过随机资源分配学习平均处理效应，并优化因果评估与服务定向之间的权衡。
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在帮助有需要者的同时学习处理效应
- 研究问题
- 社会服务资源有限时，如何设计随机分配规则，在优先帮助高需求人群与准确估计项目平均处理效应之间取得可量化的权衡？
- 方法与思路
- 作者优化随机分配政策，构建需求定向与效应学习的帕累托前沿，给出政策学习的样本复杂度保证和高效实现，并用真实服务数据评估。
- 材料陈述的贡献
- 摘要称优化政策可缓解两项目标的冲突；示例可保留九成最优定向效用，同时使效应估计所需样本少于随机试验的两倍。
- 阅读关注点
- 核对需求效用与学习目标的定义、前沿约束及样本复杂度条件，检查真实服务数据上的评估设计和九成效用示例的适用范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要中的数值属于特定示例，未说明完整条件，不能作为所有分配政策的普遍保证。
原文摘要
Many social programs attempt to allocate scarce resources to people with the greatest need. Indeed, public services increasingly use algorithmic risk assessments motivated by this goal. However, targeting the highest-need recipients often conflicts with attempting to evaluate the causal effect of the program as a whole, as the best evaluations would be obtained by randomizing the allocation. We propose a framework to design randomized allocation rules which optimally balance targeting high-need individuals with learning treatment effects, presenting policymakers with a Pareto frontier between the two goals. We give sample complexity guarantees for the policy learning problem and provide a computationally efficient strategy to implement it. We then collaborate with the human services department of Allegheny County, Pennsylvania to evaluate our methods on data from real service delivery settings. Optimized policies can substantially mitigate the tradeoff between learning and targeting. For example, it is often possible to obtain 90% of the optimal utility in targeting high-need individuals while ensuring that the average treatment effect can be estimated with less than 2 times the samples that a randomized controlled trial would require. Mechanisms for targeting public services often focus on measuring need as accurately as possible. However, our results suggest that algorithmic systems in public services can be most impactful if they incorporate program evaluation as an explicit goal alongside targeting.