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Matching on Generalized Propensity Scores with Continuous Exposures

连续暴露下基于广义倾向得分的匹配

In the context of a binary treatment, matching is a well-established approach in causal inference. However, in the context of a continuous treatment or exposure, matching is still underdeveloped. We propose an innovative matching approach to estimate an average causal exposure-response function under the setting of continuous exposures that relies on the generalized propensity score (GPS). Our approach maintains the following attractive features of matching: (a) clear…

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Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning

利用因果推断与机器学习评估1990年《清洁空气法》修正案的健康影响

We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the Total Events Avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures…

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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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Bayesian Inference for Sequential Treatments Under Latent Sequential Ignorability

潜在序贯可忽略性下序贯处理的贝叶斯推断

Federico Ricciardia* , Alessandra Matteib & Fabrizia Mealliba Department of Statistical Science, University College London, London, UK; b Department of Statistics, Computer Science, Applications, University of Florence, Florence, ItalySupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Federico Ricciardi f.ricciardi@ucl.ac.uk Department of Statistical Science, University College London, London, WC1E 7HB,…

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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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Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance

利用次要结局改进存在不依从的随机实验推断

We develop new methods for analyzing randomized experiments with noncompliance and, by extension, instrumental variable settings, when the often controversial, but key, exclusion restriction assumption is violated. We show how existing large-sample bounds on intention-to-treat effects for the subpopulations of compliers, never-takers, and always-takers can be tightened by exploiting the joint distribution of the outcome of interest and a secondary outcome, for which the…

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A Bayesian Semiparametric Approach to Intermediate Variables in Causal Inference

因果推断中连续中间变量的贝叶斯半参数建模

In causal inference studies, treatment comparisons often need to be adjusted for confounded post-treatment variables. Principal stratification (PS) is a framework to deal with such variables within the potential outcome approach to causal inference. Continuous intermediate variables introduce inferential challenges to PS analysis. Existing methods either dichotomize the intermediate variable, or assume a fully parametric model for the joint distribution of the potential…

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Augmented Designs to Assess Principal Strata Direct Effects

用于评估主层直接效应的增强实验设计

Summary Many studies involving causal questions are often concerned with understanding the causal pathways by which a treatment affects an outcome. Thus, the concept of ‘direct’versus‘indirect’ effects comes into play. We tackle the problem of disentangling direct and indirect effects by investigating new augmented experimental designs, where the treatment is randomized, and the mediating variable is not forced, but only randomly encouraged. There are two key features of our…

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Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification

利用主分层对职业培训项目因果效应进行似然分析

Government-sponsored job-training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public. The evaluation usually focuses on employment and total earnings, although the effect on wages is also of interest, because this effect reflects the increase in human capital due to the training program, whereas the effect on total earnings may be simply reflecting the increased likelihood of employment without any effect on wage…

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From temporary help jobs to permanent employment: what can we learn from matching estimators and their sensitivity?

从临时派遣工作到永久就业:匹配估计及其敏感性

Abstract The diffusion of temporary work agency (TWA) jobs has led to a harsh policy debate and ambiguous empirical evidence. Results for the USA, based on quasi‐experimental evidence, suggest that a TWA assignment decreases the probability of finding a stable job, while results for Europe, based on the conditional independence assumption (CIA), typically reach opposite conclusions. Using data for two Italian regions, we rely on a matching estimator to show that TWA…

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Occupational pensions and job mobility in Britain: Estimation of a random-effects competing risks model

英国职业养老金与工作流动:随机效应竞争风险模型估计

We analyse transitions between pensionable jobs, non-pensionable jobs, and other labour market states, using the 1988/9 UK Retirement Survey. We focus on the positive association between length of job tenure and pensionable status, allowing for the possibility that pension scheme members are less mobile than other workers because they have persistent unobserved characteristics that predispose them towards a high degree of security in both employment and retirement. We use a…

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Occupational pensions and job mobility in Britain: Estimation of a random‐effects competing risks model

英国职业养老金与工作流动:随机效应竞争风险模型估计

We analyse transitions between pensionable jobs, non-pensionable jobs, and other labour market states, using the 1988/9 UK Retirement Survey. We focus on the positive association between length of job tenure and pensionable status, allowing for the possibility that pension scheme members are less mobile than other workers because they have persistent unobserved characteristics that predispose them towards a high degree of security in both employment and retirement. We use a…

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