# Johannes Textor

Causal Discovery From Bivariate Relationships

In Causal Discovery, we ask which models from a certain causal
model class

(e.g., DAGs) would be consistent with a given dataset. Many
causal discovery

algorithms are based on conditional independence testing.
However, conditional

independence is difficult to test, especially when parametric
assumptions like

normality cannot be made. Hence, we ask to what extent causal
discovery is still

possible when we restrict our attention to only pairwise
relationships, for

which a wide variety of both parametric and non-parametric
statistical

independence tests are available. Suprisingly, we find that the
entire class of

edge-maximal DAGs that are consistent with a given set of
pairwise dependencies

can be described by a single graph, which can be constructed by
a rather simple

algorithm. Furthermore, we give a precise characterization of
how much

discrimination power we lose by not looking at conditional
independencies.

Finally, we empirically investigate the failed discovery rate of
the pairwise

approach -- assuming a correct DAG exists, how often is it
rejected? -- and

compare the results to those the partial correlation based PC
algorithm.