  The standard way to parameterize the distributions represented by a directed
acyclic graph is to insert a parametric family for the conditional distribution
of each random variable given its parents. We show that when one's goal is to
test for or estimate an effect of a sequentially applied treatment, this
natural parameterization has serious deficiencies. By reparameterizing the
graph using structural nested models, these deficiencies can be avoided.
