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首页> 外文期刊>Frontiers in Psychology >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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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：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

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摘要

One practical challenge in observational studies and quasi-experimental designs is selection bias. The issue of selection bias becomes more concerning when data are non-normal and contain missing values. Recently, a Bayesian robust two-stage causal modeling with instrumental variables was developed and has the advantages of addressing selection bias and handle non-normal data and missing data simultaneously in one model. The method provides reliable parameter and standard error estimates when missing data and outliers exist. The modeling technique can be widely applied to empirical studies particularly in social, psychological and behavioral areas where any of the three issues (e.g., selection bias, data with outliers and missing data) is commonly seen. To implement this method, we developed an R package named ALMOND ( A nalysis of L ATE (Local Average Treatment Effect) for M issing O r/and N onnormal D ata). Package users have the flexibility to directly apply the Bayesian robust two-stage causal models or write their own Bayesian models from scratch within the package. To facilitate the application of the Bayesian robust two-stage causal modeling technique, we provide a tutorial for the ALMOND package in this article, and illustrate the application with two examples from empirical research.
机译：观察研究和准实验设计中的一个实际挑战是选择偏差。当数据是非正常并且包含缺失值时，选择偏差问题更加有关。最近，开发了一种具有乐器变量的贝叶斯强大的两级因果建模，并具有在一个型号中同时寻址选择偏差并处理非正常数据和丢失数据的优点。该方法提供可靠的参数和标准错误估计，当存在丢失的数据和异常值时。建模技术可以广泛应用于常规，心理和行为领域的经验研究，其中包括三个问题中的任何一个（例如，选择偏见，具有异常值的数据和缺失数据）。为了实现这种方法，我们开发了名为Almond的R包（用于M次数为O R /和N OnnorMal D ATA的L ATE（局部平均处理效果）的NALY分析）。包用户具有可灵活地直接应用贝叶斯稳健的两级因果模型，或从包装内从头开始写自己的贝叶斯模型。为方便贝叶斯稳健的两级因果建模技术的应用，我们为本文章中的杏仁套餐提供了一个教程，并说明了与实证研究的两个例子的应用。

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