Causal inference methods to study nonrandomized, preexisting development interventions
Causal inference methods to study nonrandomized, preexisting development interventions
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DOI：
10.1073/pnas.1008944107
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发表时间：
2010-12-28
影响因子：
11.1
通讯作者：
Colford, John M., Jr.
中科院分区：
文献类型：
--
作者：
Arnold, Benjamin F.;Khush, Ranjiv S.;Colford, John M., Jr.
有必要对解决重大全球健康和发展问题的干预措施进行经验衡量，以确保资源得到适当应用。这类干预项目通常在团体或社区层面展开。衡量社区一级干预措施有效性的黄金标准设计是社区随机试验，但这些试验的条件往往使其难以评估外部有效性和可持续性。相对于随机研究而言，社区干预的绝对数量表明，需要严格的观察方法来衡量其影响。在本文中，我们使用潜在结果模型进行因果推理，以激发匹配队列设计来研究非随机、预先存在的干预措施的影响和可持续性。我们用印度农村的卫生动员、供水和卫生干预来说明这种方法。在25个村庄的匹配样本中，我们招募了1284名儿童
Empirical measurement of interventions to address significant global health and development problems is necessary to ensure that resources are applied appropriately. Such intervention programs are often deployed at the group or community level. The gold standard design to measure the effectiveness of community-level interventions is the community-randomized trial, but the conditions of these trials often make it difficult to assess their external validity and sustainability. The sheer number of community interventions, relative to randomized studies, speaks to a need for rigorous observational methods to measure their impact. In this article, we use the potential outcomes model for causal inference to motivate a matched cohort design to study the impact and sustainability of nonrandomized, preexisting interventions. We illustrate the method using a sanitation mobilization, water supply, and hygiene intervention in rural India. In a matched sample of 25 villages, we enrolled 1,284 children