Econometrica · 2026 · Takanori Ida、Takunori Ishihara、Koichiro Ito、Daido Kido、Toru Kitagawa、Shosei Sakaguchi
中文摘要
我们构建了一项最优政策配置规则，整合了经济学中常用的两种不同方法——依据可观测特征进行定向配置，以及通过自我选择进行定向配置。该方法可结合实验或准实验数据使用，以识别为实现政策制定者的目标，谁应接受处理、谁不应接受处理、谁应进行自我选择。将该方法应用于一项居民能源返利项目的随机对照试验，我们发现，同时以最优方式利用可观测数据与自我选择的定向配置优于传统定向配置。我们运用局部平均处理效应（LATE）框架（Imbens and Angrist (1994)）考察本方法的作用机制。基于实验产生的随机变异，通过估计若干关键的 LATE，我们说明了本方法如何使政策制定者识别出哪些人的自我选择会增进社会福利、哪些人的自我选择会损害社会福利。
Abstract
We develop an optimal policy assignment rule that integrates two distinctive approaches commonly used in economics—targeting by observables and targeting through self‐selection . Our method can be used with experimental or quasi‐experimental data to identify who should be treated, be untreated, and self‐select to achieve a policymaker's objective. Applying this method to a randomized controlled trial on a residential energy rebate program, we find that targeting that optimally exploits both observable data and self‐selection outperforms conventional targeting. We use the Local Average Treatment Effect (LATE) framework (Imbens and Angrist (1994)) to investigate the mechanism in our approach. By estimating several key LATEs based on the random variation created by our experiment, we demonstrate how our method allows policymakers to identify whose self‐selection would be valuable and harmful to social welfare.