  Causal inference methods based on conditional independence construct Markov
equivalent graphs, and cannot be applied to bivariate cases. The approaches
based on independence of cause and mechanism state, on the contrary, that
causal discovery can be inferred for two observations. In our contribution, we
challenge to reconcile these two research directions. We study the role of
latent variables such as latent instrumental variables and hidden common causes
in the causal graphical structures. We show that the methods based on the
independence of cause and mechanism, indirectly contain traces of the existence
of the hidden instrumental variables. We derive a novel algorithm to infer
causal relationships between two variables, and we validate the proposed method
on simulated data and on a benchmark of cause-effect pairs. We illustrate by
our experiments that the proposed approach is simple and extremely competitive
in terms of empirical accuracy compared to the state-of-the-art methods.
