  Identifying the main features and learning the causal relationships of a
dynamic system from time-series of sensor data are key problems in many
real-world robot applications. In this paper, we propose an extension of a
state-of-the-art causal discovery method, PCMCI, embedding an additional
feature-selection module based on transfer entropy. Starting from a prefixed
set of variables, the new algorithm reconstructs the causal model of the
observed system by considering only its main features and neglecting those
deemed unnecessary for understanding the evolution of the system. We first
validate the method on a toy problem and on synthetic data of brain network,
for which the ground-truth models are available, and then on a real-world
robotics scenario using a large-scale time-series dataset of human
trajectories. The experiments demonstrate that our solution outperforms the
previous state-of-the-art technique in terms of accuracy and computational
efficiency, allowing better and faster causal discovery of meaningful models
from robot sensor data.
