Railway transportation is a mobility solution that must be both reliable and safe. To this end, the technical field of predictive maintenance focuses on applying data science to maximize the availability of rolling stock assets. This leads to modeling their degradation and minimizing their downtime by preventing service-affecting failures. Artificial Intelligence and Machine Learning have proven to be effective techniques for extracting latent patterns from the available data, but the observed data by itself can sometimes be incomplete and misleading. In this sense, Causal Inference emerges as an avenue of improvement to shed light into the limitations and pitfalls of data-driven predictive approaches, and ultimately help maintainers make better informed decisions to manage the fleet.