Subaerial biofilms (SABs) are microbial communities that form on surfaces exposed to both air and periodic moisture and that can adapt to harsh environmental conditions like UV radiation, and fluctuating temperatures. On the one hand they can protect built surfaces by forming a barrier against environmental stressors, on the other they can also cause deterioration through biological weathering. The balance is complex and depend on a large number of factors. Unfortunately, only a small part of the complex multiscale network of physical, chemical and biological processes is captured by existing mechanistic model; this prompts for the involvement of phenomenological models. In this work we point at the modeling advantages offered by Bayesian Networks (BNs), Causal Networks and Targeted Learning (TL) in the study of the dual role of SABs.