  The dynamics of supercooled liquids slow down and become increasingly
heterogeneous as they are cooled. Recently, local structural variables
identified using machine learning, such as "softness", have emerged as
predictors of local dynamics. Here we construct a model using softness to
describe the structural origins of dynamical heterogeneity in supercooled
liquids. In our model, the probability of particles to rearrange is determined
by their softness, and each rearrangement induces changes in the softness of
nearby particles, describing facilitation. We show how to ensure that these
changes respect the underlying time-reversal symmetry of the liquid's dynamics.
The model reproduces the salient features of dynamical heterogeneity, and
demonstrates how long-ranged dynamical correlations can emerge at long time
scales from a relatively short softness correlation length.
