  Cross-sectional studies are widely prevalent since they are more feasible to
conduct compared to longitudinal studies. However, cross-sectional data lack
the temporal information required to study the evolution of the underlying
processes. Nevertheless, this is essential to develop predictive computational
models which is the first step towards causal modelling. We propose a method
for inferring computational models from cross-sectional data using Langevin
dynamics. This method can be applied to any system that can be described as
effectively following a free energy landscape, such as protein folding, stem
cell differentiation and reprogramming, and social systems involving human
interaction and social norms. A crucial assumption in our method is that the
data-points are gathered from a system in (local) equilibrium. The result is a
set of stochastic differential equations which capture the temporal dynamics,
by assuming that groups of data-points are subject to the same free energy
landscape and amount of noise. Our method is a 'baseline' method which
initiates the development of computational models which can be iteratively
enhanced through the inclusion of expert knowledge. We validate the proposed
method against two population-based longitudinal datasets and observe
significant predictive power in comparison with random choice algorithms. We
also show how the predictive power of our 'baseline' model can be enhanced by
incorporating domain expert knowledge. Our method addresses an important
obstacle for model development in fields dominated by cross-sectional datasets.
