  Modelling growth in student achievement is a significant challenge in the
field of education. Understanding how interventions or experiences such as
part-time work can influence this growth is also important. Traditional methods
like difference-in-differences are effective for estimating causal effects from
longitudinal data. Meanwhile, Bayesian non-parametric methods have recently
become popular for estimating causal effects from single time point
observational studies. However, there remains a scarcity of methods capable of
combining the strengths of these two approaches to flexibly estimate
heterogeneous causal effects from longitudinal data. Motivated by two waves of
data from the High School Longitudinal Study, the NCES' most recent
longitudinal study which tracks a representative sample of over 20,000 students
in the US, our study introduces a longitudinal extension of Bayesian Causal
Forests. This model allows for the flexible identification of both individual
growth in mathematical ability and the effects of participation in part-time
work. Simulation studies demonstrate the predictive performance and reliable
uncertainty quantification of the proposed model. Results reveal the negative
impact of part time work for most students, but hint at potential benefits for
those students with an initially low sense of school belonging. Clear signs of
a widening achievement gap between students with high and low academic
achievement are also identified. Potential policy implications are discussed,
along with promising areas for future research.
