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]).
翻译:BCF是一种基于高度灵活的非参数回归和分类工具,称为Bayesian Additive Reture Regive 树(BART)的因果推断机学习模型。根据国际数学和科学研究趋势(TIMSS)的数据(TIMSS),其中包括数学和科学学生成绩的数据,我们提出了BCF算法的多变量扩展。在模拟研究的帮助下,我们发现我们的方法可以准确估计受相同待遇的多种结果的因果影响。我们还将我们的模型应用于爱尔兰数据(TIMSS 2019)。我们的调查结果显示,在爱尔兰国内使用研究台(数学ATE 95% CI:[0.20、11.67] )的积极效果,同时也强调了学生在学校常常感到饥饿的负面后果(数学ATE 95% CI:[11.15, -2.78] 科学ATE 95% CI:[10.82,-172] 或经常缺数据(数学ATE 95% CI:[12.47,-1.5)。</s>