  Discovering latent representations of the observed world has become
increasingly more relevant in data analysis. Much of the effort concentrates on
building latent variables which can be used in prediction problems, such as
classification and regression. A related goal of learning latent structure from
data is that of identifying which hidden common causes generate the
observations, such as in applications that require predicting the effect of
policies. This will be the main problem tackled in our contribution: given a
dataset of indicators assumed to be generated by unknown and unmeasured common
causes, we wish to discover which hidden common causes are those, and how they
generate our data. This is possible under the assumption that observed
variables are linear functions of the latent causes with additive noise.
Previous results in the literature present solutions for the case where each
observed variable is a noisy function of a single latent variable. We show how
to extend the existing results for some cases where observed variables measure
more than one latent variable.
