  Causal knowledge is vital for effective reasoning in science, as causal
relations, unlike correlations, allow one to reason about the outcomes of
interventions. Algorithms that can discover causal relations from observational
data are based on the assumption that all variables have been jointly measured
in a single dataset. In many cases this assumption fails. Previous approaches
to overcoming this shortcoming devised algorithms that returned all joint
causal structures consistent with the conditional independence information
contained in each individual dataset. But, as conditional independence tests
only determine causal structure up to Markov equivalence, the number of
consistent joint structures returned by these approaches can be quite large.
The last decade has seen the development of elegant algorithms for discovering
causal relations beyond conditional independence, which can distinguish among
Markov equivalent structures. In this work we adapt and extend these so-called
bivariate causal discovery algorithms to the problem of learning consistent
causal structures from multiple datasets with overlapping variables belonging
to the same generating process, providing a sound and complete algorithm that
outperforms previous approaches on synthetic and real data.
