  This paper frames causal structure estimation as a machine learning task. The
idea is to treat indicators of causal relationships between variables as
`labels' and to exploit available data on the variables of interest to provide
features for the labelling task. Background scientific knowledge or any
available interventional data provide labels on some causal relationships and
the remainder are treated as unlabelled. To illustrate the key ideas, we
develop a distance-based approach (based on bivariate histograms) within a
manifold regularization framework. We present empirical results on three
different biological data sets (including examples where causal effects can be
verified by experimental intervention), that together demonstrate the efficacy
and general nature of the approach as well as its simplicity from a user's
point of view.
