  Independent component analysis provides a principled framework for
unsupervised representation learning, with solid theory on the identifiability
of the latent code that generated the data, given only observations of mixtures
thereof. Unfortunately, when the mixing is nonlinear, the model is provably
nonidentifiable, since statistical independence alone does not sufficiently
constrain the problem. Identifiability can be recovered in settings where
additional, typically observed variables are included in the generative
process. We investigate an alternative path and consider instead including
assumptions reflecting the principle of independent causal mechanisms exploited
in the field of causality. Specifically, our approach is motivated by thinking
of each source as independently influencing the mixing process. This gives rise
to a framework which we term independent mechanism analysis. We provide
theoretical and empirical evidence that our approach circumvents a number of
nonidentifiability issues arising in nonlinear blind source separation.
