Complex systems often contain feedback loops, that can be described as cyclic causal models. Contrary to acyclic graphs, intervening in cyclic graphs may lead to counterproductive effects, which cannot be inferred directly from the graph structure. After establishing a framework for differentiable interventions based on Lie groups, we take advantage of modern automatic differentiation techniques and their application to implicit functions in order to optimize interventions in cyclic causal models. We illustrate this framework by investigating the scenarios of transition to sustainable economies.