  Information Geometric Causal Inference (IGCI) is a new approach to
distinguish between cause and effect for two variables. It is based on an
independence assumption between input distribution and causal mechanism that
can be phrased in terms of orthogonality in information space. We describe two
intuitive reinterpretations of this approach that makes IGCI more accessible to
a broader audience.
  Moreover, we show that the described independence is related to the
hypothesis that unsupervised learning and semi-supervised learning only works
for predicting the cause from the effect and not vice versa.
