Estimation of Effects of Sequential Treatments by
Reparameterizing Directed Acyclic Graphs
James M. Robins and Larry Wasserman
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.
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