  Reconstructing accurate causal models of dynamic systems from time-series of
sensor data is a key problem in many real-world scenarios. In this paper, we
present an overview based on our experience about practical challenges that the
causal analysis encounters when applied to autonomous robots and how Continual
Learning~(CL) could help to overcome them. We propose a possible way to
leverage the CL paradigm to make causal discovery feasible for robotics
applications where the computational resources are limited, while at the same
time exploiting the robot as an active agent that helps to increase the quality
of the reconstructed causal models.
