Uncertainty in Artificial Intelligence
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Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs
James Robins, Larry Wasserman
Abstract:
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.
Keywords: Causal inference, semi-parametric inference.
Pages: 409-420
PS Link: http://lib.stat.cmu.edu/www/cmu-stats/tr/tr654/tr654.html
PDF Link: /papers/97/p409-robins.pdf
BibTex:
@INPROCEEDINGS{Robins97,
AUTHOR = "James Robins and Larry Wasserman",
TITLE = "Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "409--420"
}


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