Egalitarian Equivalent Treatment Effects: The Econometrics of Inequality-Sensitive Treatment Effects Estimation

决策者通常对理解不同实践的分配效应有浓厚的兴趣，但现有的这些实验分析往往要么没有充分考虑到这些问题，要么缺乏一个清晰的经济框架来评估它们。拟议的研究试图通过建立一个统计框架来分析干预措施的分配影响，建立在现代福利经济理论的基础上，弥合这一差距。这将提供一种更加全面和严格的评价方法，为分析带来效率和分配问题。拟议的工作将由三个部分组成。第一部分是与Marc Fleurbaey合作编写的，涉及开发一种方法，用于确定评估者的社会偏好，该方法基于评估者认为可接受的不同个体福祉之间的权衡。第二个组成部分涉及将这些社会福利信息纳入统计估计框架，使用评估者偏好的平等主义等效表示，这是一个类似于预期效用理论中的确定性等效表示的概念。第三部分涉及将该框架应用于随机对照试验中最优治疗规则的分析，使用贝叶斯、最大和最小最大后悔标准，并在几个表现出相当大治疗效果异质性的知名试验中测试开发的方法。表现出治疗效果异质性的干预措施很难从福利方面进行评估和总结。本项目开发的方法为如何进行评估提供了具体的定量指导。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Decision makers often have a strong interest in understanding the distributional effects of different practices, but existing analyses of these experiments often either fail to adequately consider these issues or lack a clear economic framework for evaluating them. The proposed research seeks to bridge this gap by developing a statistical framework for analyzing distributional impacts in interventions, building on modern welfare economic theory. This will provide a more comprehensive and rigorous approach to evaluation that brings both efficiency and distributional concerns to the analysis.The proposed work will consist of three components. The first component, written in collaboration with Marc Fleurbaey, involves developing a methodology for determining an evaluator’s social preferences based on the tradeoffs between the well-being of different individuals that the evaluator considers acceptable. The second component involves incorporating this social welfare information into a statistical estimation framework, using an egalitarian equivalent representation of the evaluator’s preferences, a concept akin to the certainty equivalent representation in expected utility theory. The third component involves applying this framework to the analysis of optimal treatment rules in randomized controlled trials, using Bayesian, maximin, and minimax regret criteria, and testing the developed methodologies on several well-known trials that exhibit considerable treatment effect heterogeneity. Interventions that exhibit treatment effect heterogeneity can be difficult to evaluate and summarize in terms of welfare. The methods developed in this project provide specific, quantitative guidance for how to do this evaluation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.