In Burkina Faso, political unrest, conflict, and violence are regular threats to stability. This produces environments which can change the risk calculus towards family planning. To anticipate population and humanitarian needs during and after tumult, it’s important to understand the effect of violence on contraceptive use and intentions towards family planning. However, the effects of violence can be widespread and have varying influence across a region. The processes these are linked by are inherently spatiotemporal. Causal inference in this setting is not well developed, thus the aim of this project is to put forth a nonparametric methodology and framework for causal estimation in spatiotemporal phenomena via a graphical modelling approach. Spatiotemporal systems violate the assumptions that go into a causal analysis such as the Stable Unit Treatment Value Assumption (SUTVA). Prior work in the potential outcome framework shows how to accommodate the quirks of spatiotemporal processes. This research aims for the same using graphical models. A framework using graphical models can equip decision makers with simple and accessible tools for explaining complicated processes. This approach simplifies combining statistical and subject matter expertise in a nonparametric framework.