  Over the past two decades, several consistent procedures have been designed
to infer causal conclusions from observational data. We prove that if the true
causal network might be an arbitrary, linear Gaussian network or a discrete
Bayes network, then every unambiguous causal conclusion produced by a
consistent method from non-experimental data is subject to reversal as the
sample size increases any finite number of times. That result, called the
causal flipping theorem, extends prior results to the effect that causal
discovery cannot be reliable on a given sample size. We argue that since
repeated flipping of causal conclusions is unavoidable in principle for
consistent methods, the best possible discovery methods are consistent methods
that retract their earlier conclusions no more than necessary. A series of
simulations of various methods across a wide range of sample sizes illustrates
concretely both the theorem and the principle of comparing methods in terms of
retractions.
