Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets using Bayesian Principal Stratification.
Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets using Bayesian Principal Stratification.
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DOI：
10.1080/01621459.2015.1125788
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发表时间：
2016
影响因子：
3.7
通讯作者：
VanderWeele TJ
中科院分区：
文献类型：
--
作者：
Forastiere L;Mealli F;VanderWeele TJ
探索因果机制对于研究人员和政策制定者了解干预措施如何发挥作用以及如何加以改进通常很重要。这个任务在集群激励设计（CED）中可能是至关重要的。鼓励设计研究经常出现时，治疗不能强制执行，因为道德或实际的限制和鼓励干预（宣传活动，激励措施等）的目的是增加感兴趣的治疗的摄取。从设计上看，鼓励总是会带来不合规的复杂性。鼓励措施还可以产生各种机制，特别是在分组一级进行鼓励时。同一集群内各单位之间的社会互动可能会产生溢出效应。通过溢出效应将鼓励的效果与通过加强治疗的效果分开，将使人们更好地了解干预措施，这对规划方案的扩大阶段可能是有说服力的。在CED和不遵守以前的工作的基础上，我们使用的主要分层框架来定义层特定的因果效应，即，特定的潜在亚群的影响，定义的联合潜在的遵守状态下的两个鼓励条件。我们展示了后者特定层的因果效应是如何与文献中常用的分解相关的，并提供了灵活的同质性假设，在此假设下，跨主要层的外推可以解开这些效应。因果被估量的估计可以用贝叶斯推理方法进行，使用分层模型来解释聚类。我们说明了所提出的方法，通过分析在赞比亚实施的一个集群随机实验，旨在评估旨在增加蚊帐覆盖率的农业贷款计划对疟疾流行的影响。分配到该计划的农户可以利用延期付款和购买新蚊帐的折扣。我们的分析表明，缺乏证据表明，通过溢出效应，即通过在社区中更大的蚊帐覆盖率，向一组家庭提供该计划的效果。
Exploration of causal mechanisms is often important for researchers and policymakers to understand how an intervention works and how it can be improved. This task can be crucial in clustered encouragement designs (CED). Encouragement design studies arise frequently when the treatment cannot be enforced because of ethical or practical constrains and an encouragement intervention (information campaigns, incentives, etc) is conceived with the purpose of increasing the uptake of the treatment of interest. By design, encouragements always entail the complication of non-compliance. Encouragements can also give rise to a variety of mechanisms, particularly when encouragement is assigned at cluster level. Social interactions among units within the same cluster can result in spillover effects. Disentangling the effect of encouragement through spillover effects from that through the enhancement of the treatment would give better insight into the intervention and it could be compelling for planning the scaling-up phase of the program. Building on previous works on CEDs and non-compliance, we use the principal stratification framework to define stratum-specific causal effects, that is, effects for specific latent subpopulations, defined by the joint potential compliance statuses under both encouragement conditions. We show how the latter stratum-specific causal effects are related to the decomposition commonly used in the literature and provide flexible homogeneity assumptions under which an extrapolation across principal strata allows one to disentangle the effects. Estimation of causal estimands can be performed with Bayesian inferential methods using hierarchical models to account for clustering. We illustrate the proposed methodology by analyzing a cluster randomized experiment implemented in Zambia and designed to evaluate the impact on malaria prevalence of an agricultural loan program intended to increase the bed net coverage. Farmer households assigned to the program could take advantage of a deferred payment and a discount in the purchase of new bed nets. Our analysis shows a lack of evidence of an effect of the offering of the program to a cluster of households through spillover effects, that is through a greater bed net coverage in the neighborhood.
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影响因子：
7.6
作者：
BARON, RM;KENNY, DA
通讯作者：
KENNY, DA
影响因子：
2
作者：
Dunn, Graham;Bentall, Richard
通讯作者：
Bentall, Richard
影响因子：
7
作者：
Imai, Kosuke;Keele, Luke;Tingley, Dustin
通讯作者：
Tingley, Dustin
DOI：
10.4269/ajtmh.1998.59.80
发表时间：
1998-07-01
影响因子：
3.3
作者：
Binka, FN;Indome, F;Smith, T
通讯作者：
Smith, T
DOI：
10.1016/s0035-9203(00)90103-2
发表时间：
2000-07-01
影响因子：
2.2
作者：
Howard, SC;Omumbo, J;Snow, RW
通讯作者：
Snow, RW