  Quantum causality is an emerging field of study which has the potential to
greatly advance our understanding of quantum systems. One of the most important
problems in quantum causality is linked to this prominent aphorism that states
correlation does not mean causation. A direct generalization of the existing
causal inference techniques to the quantum domain is not possible due to
superposition and entanglement. We put forth a new theoretical framework for
merging quantum information science and causal inference by exploiting entropic
principles. For this purpose, we leverage the concept of conditional density
matrices to develop a scalable algorithmic approach for inferring causality in
the presence of latent confounders (common causes) in quantum systems. We apply
our proposed framework to an experimentally relevant scenario of identifying
message senders on quantum noisy links, where it is validated that the input
before noise as a latent confounder is the cause of the noisy outputs. We also
demonstrate that the proposed approach outperforms the results of classical
causal inference even when the variables are classical by exploiting quantum
dependence between variables through density matrices rather than joint
probability distributions. Thus, the proposed approach unifies classical and
quantum causal inference in a principled way. This successful inference on a
synthetic quantum dataset can lay the foundations of identifying originators of
malicious activity on future multi-node quantum networks.
