  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]).
