Distributed computing continuum systems combine various computational layers into one cohesive platform. Thus, clients can benefit both from low-latency computations, i.e., from Edge devices, as well as highly available and virtually unlimited resources, i.e., from the Cloud. Nevertheless, as components become more distributed over a computing architecture, enforcing requirements (e.g., low latency or energy consumption) becomes equally challenging. To that extent, we propose causal mechanisms that identify how computing services can ensure their internal requirements, as well as how they impact other components through their actions. We empirically train these probabilistic models through a neuroscience framework -- Active Inference -- that promises quick convergence and verifiable behavior.