  Offline data are both valuable and practical resources for teaching robots
complex behaviors. Ideally, learning agents should not be constrained by the
scarcity of available demonstrations, but rather generalize beyond the training
distribution. However, the complexity of real-world scenarios typically
requires huge amounts of data to prevent neural network policies from picking
up on spurious correlations and learning non-causal relationships. We propose
CAIAC, a data augmentation method that can create feasible synthetic
transitions from a fixed dataset without having access to online environment
interactions. By utilizing principled methods for quantifying causal influence,
we are able to perform counterfactual reasoning by swapping
$\it{action}$-unaffected parts of the state-space between independent
trajectories in the dataset. We empirically show that this leads to a
substantial increase in robustness of offline learning algorithms against
distributional shift.
