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If $X,Y,Z$ denote sets of random variables, two different data sources maycontain samples from $P_{X,Y}$ and $P_{Y,Z}$, respectively. We argue thatcausal discovery can help inferring properties of the `unobserved jointdistributions' $P_{X,Y,Z}$ or $P_{X,Z}$. The properties may be conditionalindependences (as in `integrative causal inference') or also quantitativestatements about dependences.  More generally, we define a learning scenario where the input is a subset ofvariables and the label is some statistical property of that subset. Sets ofjointly observed variables define the training points, while unobserved setsare possible test points. To solve this learning task, we infer, as anintermediate step, a causal model from the observations that then entailsproperties of unobserved sets. Accordingly, we can define the VC dimension of aclass of causal models and derive generalization bounds for the predictions.  Here, causal discovery becomes more modest and better accessible to empiricaltests than usual: rather than trying to find a causal hypothesis that is `true'a causal hypothesis is {\it useful} whenever it correctly predicts statisticalproperties of unobserved joint distributions. This way, a sparse causal graphthat omits weak influences may be more useful than a dense one (despite beingless accurate) because it is able to reconstruct the full joint distributionfrom marginal distributions of smaller subsets.  Within such a `pragmatic' application of causal discovery, some popularheuristic approaches become justified in retrospect. It is, for instance,allowed to infer DAGs from partial correlations instead of conditionalindependences if the DAGs are only used to predict partial correlations.

Reinterpreting causal discovery as the task of predicting unobserved joint statistics