Bayesian causal forests for multivariate outcomes: application to Irish data from an international large scale education assessment
该研究将贝叶斯因果森林扩展至多元结果，利用TIMSS 2019爱尔兰数据分析家庭学习资源与在校状况对学生数学和科学成绩的因果效应。
Abstract Bayesian Causal Forests (BCF) is a causal inference machine learning model based on the flexible non-parametric regression and classification tool, 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 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: [−0.50, 10.14]) while also highlighting the negative consequences of students often feeling hungry at school (Mathematics ATE 95% CI: [−8.86, −1.56] , Science ATE 95% CI: [−10.35, −0.94]) or often being absent (Mathematics ATE 95% CI: [−11.88, −2.27]). Code for replicating the results can be found at https://github.com/Nathan-McJames/MVBCF-Paper.