  We introduce an analytic pipeline to model and simulate youth trajectories
through the New York state foster care system. Our goal in doing so is to
forecast how proposed interventions may impact the foster care system's ability
to achieve it's stated goals \emph{before these interventions are actually
implemented and impact the lives of thousands of youth}. Here, we focus on two
specific stated goals of the system: racial equity, and, as codified most
recently by the 2018 Family First Prevention Services Act (FFPSA), a focus on
keeping all youth out of foster care. We also focus on one specific potential
intervention -- a predictive model, proposed in prior work and implemented
elsewhere in the U.S., which aims to determine whether or not a youth is in
need of care. We use our method to explore how the implementation of this
predictive model in New York would impact racial equity and the number of youth
in care. While our findings, as in any simulation model, ultimately rely on
modeling assumptions, we find evidence that the model would not necessarily
achieve either goal. Primarily, then, we aim to further promote the use of
data-driven simulation to help understand the ramifications of algorithmic
interventions in public systems.
