选择最有效的助推: 来自大规模免疫实验的证据

Econometrica · 2025 · [{"name": "Abhijit Banerjee", "affiliation": []}, {"name": "Arun G. Chandrasekhar", "affiliation": ["Stanford Health Care", "Stanford Medicine"]}, {"name": "Suresh Dalpath", "affiliation": ["Government of Haryana"]}, {"name": "Esther Duflo", "affiliation": []}, {"name": "John Floretta", "affiliation": []}, {"name": "Matthew O. Jackson", "affiliation": ["Santa Fe Institute", "Stanford Health Care", "Stanford Medicine"]}, {"name": "Harini Kannan", "affiliation": []}, {"name": "Francine Loza", "affiliation": []}, {"name": "Anirudh Sankar", "affiliation": ["Stanford Health Care", "Stanford Medicine"]}, {"name": "Anna Schrimpf", "affiliation": []}, {"name": "Maheshwor Shrestha", "affiliation": ["World Bank"]}]

中文摘要

政策制定者往往选择一揽子政策，即由不同干预措施以不同剂量组合而成的政策包。我们提出了一种新方法——处理变体聚合（treatment variant aggregation, TVA）——用于从大型析因设计中筛选出一项政策。TVA 将不存在实质性差异的政策变体合并在一起，并剔除被判定为无效的变体。这使我们能够将关注范围限定于聚合后的政策变体，一致地估计其对结果的效应，并在校正赢家诅咒后估计最优政策效应。我们将 TVA 应用于一项大型随机对照试验，该试验检验了若干旨在刺激印度哈里亚纳邦免疫接种需求的干预措施。所考察的政策包括提醒、激励以及用于社区动员的地方大使。对这些干预措施进行交叉随机化，并为每种干预设置不同的剂量或类型，共得到 75 种组合。影响最大的政策（结合了激励、作为信息枢纽的大使以及提醒）使免疫接种次数相对于现状增加了 44%。最具成本效益的政策（信息枢纽、大使和短信提醒，但不含激励）使每美元对应的免疫接种次数相对于现状提高了 9.1%。

Abstract

Policymakers often choose a policy bundle that is a combination of different interventions in different dosages. We develop a new technique— treatment variant aggregation (TVA)—to select a policy from a large factorial design. TVA pools together policy variants that are not meaningfully different and prunes those deemed ineffective. This allows us to restrict attention to aggregated policy variants, consistently estimate their effects on the outcome, and estimate the best policy effect adjusting for the winner's curse. We apply TVA to a large randomized controlled trial that tests interventions to stimulate demand for immunization in Haryana, India. The policies under consideration include reminders, incentives, and local ambassadors for community mobilization. Cross‐randomizing these interventions, with different dosages or types of each intervention, yields 75 combinations. The policy with the largest impact (which combines incentives, ambassadors who are information hubs, and reminders) increases the number of immunizations by 44% relative to the status quo. The most cost‐effective policy (information hubs, ambassadors, and SMS reminders, but no incentives) increases the number of immunizations per dollar by 9.1% relative to the status quo.