  Discriminating between causality and correlation is a major problem in
machine learning, and theoretical tools for determining causality are still
being developed. However, people commonly make causality judgments and are
often correct, even in unfamiliar domains. What are humans doing to make these
judgments? This paper examines differences in human experts' and non-experts'
ability to attribute causality by comparing their performances to those of
machine-learning algorithms. We collected human judgments by using Amazon
Mechanical Turk (MTurk) and then divided the human subjects into two groups:
experts and non-experts. We also prepared expert and non-expert machine
algorithms based on different training of convolutional neural network (CNN)
models. The results showed that human experts' judgments were similar to those
made by an "expert" CNN model trained on a large number of examples from the
target domain. The human non-experts' judgments resembled the prediction
outputs of the CNN model that was trained on only the small number of examples
used during the MTurk instruction. We also analyzed the differences between the
expert and non-expert machine algorithms based on their neural representations
to evaluate the performances, providing insight into the human experts' and
non-experts' cognitive abilities.
