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Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks

网络观察性研究中处理效应与干扰效应的识别和估计

Laura Forastierea , Edoardo M. Airoldib* & Fabrizia Meallica Department of Biostatistics, Yale University, New Haven, CTb Department of Statistical Science, Fox School of Business, Temple University, Philadelphia, PAc Department of Statistics, Informatics, Applications “G. Parenti”, University of Florence, Florence, ItalySupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Edoardo M. Airoldi airoldi@temple.edu…

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Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum Attendance

主分层框架下的鼓励、处理与溢出效应:青少年博物馆参观实验

This article revisits results from a field experiment, conducted in Florence, Italy, to study the effects of incentives provided to high school teens to motivate them to visit art museums. In the experiment, different classes of students were randomized to three types of encouragement and were offered a free visit to a main museum in the city. Using the principal stratification framework, the article explores causal pathways that may lead students to increase future visits,…

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Principal ignorability in mediation analysis: through and beyond sequential ignorability

中介分析中的主层可忽略性:贯通并超越序贯可忽略性

In causal mediation analysis, the definitions of the natural direct and indirect effects involve potential outcomes that can never be observed, so-called a priori counterfactuals. This conceptual challenge translates into issues in identification, which requires strong and often unverifiable assumptions, including sequential ignorability. Alternatively, we can deal with post-treatment variables using the principal stratification framework, where causal effects are defined as…

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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

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…

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