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失业导致结局截断及不可忽略缺失的纵向观察研究中的因果效应评估AI 解读

Important statistical issues pervade the evaluation of training programs’ effects for unemployed people. In particular, the fact that offered wages are observed and well-defined only for subjects who are employed (truncation by death), and the problem that information on the individuals’ employment status and wage can be lost over time (attrition) raise methodological challenges for causal inference. We present an extended framework for simultaneously addressing the…

潜在序贯可忽略性下序贯处理的贝叶斯推断AI 解读

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,…

中介分析中的主层可忽略性：贯通并超越序贯可忽略性AI 解读

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…

用于评估主层直接效应的增强实验设计AI 解读

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…