Probabilistic evaluation of sequential plans from causal models with hidden variables
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the testable implications of causal models with hidden variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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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.