  Causal discovery can be a powerful tool for investigating causality when a
system can be observed but is inaccessible to experiments in practice. Despite
this, it is rarely used in any scientific or medical fields. One of the major
hurdles preventing the field of causal discovery from having a larger impact is
that it is difficult to determine when the output of a causal discovery method
can be trusted in a real-world setting. Trust is especially critical when human
health is on the line.
  In this paper, we report the results of a series of simulation studies
investigating the performance of different resampling methods as indicators of
confidence in discovered graph features. We found that subsampling and sampling
with replacement both performed surprisingly well, suggesting that they can
serve as grounds for confidence in graph features. We also found that the
calibration of subsampling and sampling with replacement had different
convergence properties, suggesting that one's choice of which to use should
depend on the sample size.
