跳到正文
← 返回论文列表与筛选
阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:摘要直接讨论不依从下的因果效应分解,以及群内社会互动引起的溢出效应。

反馈分类问题

Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification

聚类鼓励设计中因果机制的识别与估计:用贝叶斯主分层分解蚊帐干预效应

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Laura Forastiere
Fabrizia Mealli

聚类鼓励设计中因果机制的识别与估计:用贝叶斯主分层分解蚊帐干预效应

研究问题
在存在不依从和群内社会互动的聚类鼓励设计中,如何区分鼓励通过提高自身处理接受率与邻里溢出产生的效应?
方法与思路
作者用潜在依从状态定义主分层因果效应,在同质性假设下跨层外推分解机制,并用贝叶斯层级模型处理聚类。
材料陈述的贡献
摘要称建立了主分层效应与常用效应分解的联系;赞比亚蚊帐项目分析未发现支持邻里覆盖率溢出效应的证据。
阅读关注点
核对主分层定义、同质性与跨层外推假设,以及群内互动如何进入模型;关注溢出效应的不确定性和敏感性分析。
阅读边界
仅据版本未注明的 OpenAlex 索引摘要,不能推断刊本变化。摘要未说明完整识别条件;缺乏溢出证据不能解释为效应为零。

原文摘要

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.

笔记与分组
相关领域论文