  Uplift modeling requires experimental data, preferably collected in random
fashion. This places a logistical and financial burden upon any organisation
aspiring such models. Once deployed, uplift models are subject to effects from
concept drift. Hence, methods are being developed that are able to learn from
newly gained experience, as well as handle drifting environments. As these new
methods attempt to eliminate the need for experimental data, another approach
to test such methods must be formulated. Therefore, we propose a method to
simulate environments that offer causal relationships in their parameters.
