In this work, we leverage the information-theoretic notion of transfer entropy theory to study causal information flow in epidemic spreading over temporal networks. An improved understanding of causal information flow may lead to the early detection of population segments that should be monitored or immunized to enhance epidemic containment. We focus on activity driven networks, which constitute a powerful and elegant paradigm to capture the inherent time-varying nature of contacts and population heterogeneity. Our preliminary results confirm the intuition that individuals who have a higher propensity in contacting others are responsible for the largest information transfer. Moreover, we find that epidemic parameters such as the probability of infection and recovery may dominate the spreading phenomenon over heterogeneities in the contact formation.