Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment

Nathan McJames, Andrew Parnell, Yong Chen Goh, Ann O'Shea
3/8/2023
stat.MLcs.LGstat.AP

No official code found. Best implementation: Nathan-McJames/MVBCF-Paper (6 stars, last commit 2024-09-30).

Abstract

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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Nathan-McJames/MVBCF-PaperCommunity71%

Materials for replicating the results in "Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment."

62R, C++Oct 7, 20232 years ago

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Cite this paper

@article{mcjames2023bayesian,
  title  = {Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment},
  author = {Nathan McJames and Andrew Parnell and Yong Chen Goh and Ann O'Shea},
  year   = {2023},
  eprint = {2303.04874},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.04874v1}
}

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