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A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R

机译：贝叶斯探讨因果建模中缺失和非正常数据的局部平均处理效果分析：r中杏仁包的教程

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Dingjing Shi; Xin Tong; M. Joseph Meyer;
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1. A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R [J] . Dingjing Shi, Xin Tong, M. Joseph Meyer Frontiers in Psychology . 2020,第a期

机译：在因果建模中缺失和非正常数据缺失和非正常数据分析的贝叶斯方法：r中杏仁包的教程
2. A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R [J] . Shi Dingjing, Tong Xin, Meyer M. Joseph Frontiers in Psychology . 2020,第2期

机译：贝叶斯探讨因果建模中缺失和非正常数据的局部平均处理效果分析：r中杏仁包的教程
3. LOCAL MARGINAL ANALYSIS OF SPATIAL DATA: A GAUSSIAN PROCESS REGRESSION APPROACH WITH BAYESIAN MODEL AND KERNEL AVERAGING [J] . Jacob Dearmon, Tony E. Smith Advances in Econometrics . 2017,第期

机译：空间数据的局部边际分析：基于贝叶斯模型和核平均的高斯过程回归方法
4. AdDressing Missing Nodes as Missing Data in Dynamic Causal Modeling [C] . Wyatt Christopher L., Zaghlool Shaza B. 2012 Second International Workshop on Pattern Recognition in NeuroImaging . 2012

机译：在动态因果建模中将AdDressing丢失的节点作为丢失的数据
5. Skew T Based Maximum Likelihood Estimation of Latent Growth Curve Models with Non-Normal and Missing Data [D] . Henri, Maria Antoun. 2018

机译：基于基于非正常和缺失数据的潜在生长曲线模型的最大似然估计
6. A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R [O] . Dingjing Shi, Xin Tong, M. Joseph Meyer 2020

机译：因果模型中缺失和非正态数据的局部平均处理效果的贝叶斯方法分析：R中带有ALMOND包的教程
7. Semiparametric estimation of the average causal effect of treatment on an outcome measured after a postrandomization event, with missing outcome data [O] . Gilbert, Peter B., Jin, Yuying 2009

机译：在随机结果发生后测量的治疗结果的平均因果效应的半参数估计，缺少结果数据

A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R

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