多元结果的贝叶斯因果森林：应用于国际大规模教育评估的爱尔兰数据
Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment
摘要：
贝叶斯因果森林（Bayesian Causal Forests，BCF）是一种因果推断机器学习模型，基于高度灵活的非参数回归和分类工具，称为贝叶斯加性回归树（Bayesian Additive Regression Trees，BART）。从国际数学和科学研究（TIMSS）的趋势，其中包括在数学和科学的学生成绩的数据的动机，我们提出了一个多元扩展的BCF算法。在模拟研究的帮助下，我们表明，我们的方法可以准确地估计因果关系的多个结果受到相同的治疗。我们还将我们的模型应用于TIMSS 2019的爱尔兰数据。我们的研究结果揭示了在家里使用书桌的积极影响（数学ATE 95% CI：[0.20，11.67]），同时也强调了学生在学校经常感到饥饿的负面影响。（数学ATE 95% CI：[-11.15，-2.78]，科学ATE 95% CI：[-10.82，-1.72]）或经常缺失（数学ATE 95% CI：[-12.47，-1.55]）。
英文摘要：
Bayesian Causal Forests (BCF) is a causal inference machine learning model based on a highly flexible non-parametric regression and classification tool called Bayesian Additive Regression Trees (BART). Motivated by data from the Trends in International Mathematics and Science Study (TIMSS), which includes data on student achievement in both mathematics and science, we present a multivariate extension of the BCF algorithm. With the help of simulation studies we show that our approach can accurately estimate causal effects for multiple outcomes subject to the same treatment. We also apply our model to Irish data from TIMSS 2019. Our findings reveal the positive effects of having access to a study desk at home (Mathematics ATE 95% CI: [0.20, 11.67]) while also highlighting the negative consequences of students often feeling hungry at school (Mathematics ATE 95% CI: [-11.15, -2.78] , Science ATE 95% CI: [-10.82,-1.72]) or often being absent (Mathematics ATE 95% CI: [-12.47, -1.55]).
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