Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs
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.
Cite
Text
Robins and Wasserman. "Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs." Conference on Uncertainty in Artificial Intelligence, 1997.Markdown
[Robins and Wasserman. "Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs." Conference on Uncertainty in Artificial Intelligence, 1997.](https://mlanthology.org/uai/1997/robins1997uai-estimation/)BibTeX
@inproceedings{robins1997uai-estimation,
title = {{Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs}},
author = {Robins, James M. and Wasserman, Larry A.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1997},
pages = {409-420},
url = {https://mlanthology.org/uai/1997/robins1997uai-estimation/}
}