Causal Inference in the Presence of Latent Variables and Selection Bias (0)
| Venue: | In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence |
| Citations: | 24 - 4 self |
BibTeX
@INPROCEEDINGS{Spirtes_causalinference,
author = {Peter Spirtes and Christopher Meek and Thomas Richardson},
title = {Causal Inference in the Presence of Latent Variables and Selection Bias},
booktitle = {In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence},
year = {},
pages = {499--506},
publisher = {Morgan Kaufmann}
}
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Abstract
This paper uses Bayesian network models for that investigation. Bayesian networks, or directed acyclic graph (DAG) models have proved very useful in representing both causal and statistical hypotheses. The nodes of the graph represent vertices, directed edges represent direct influences, and the topology of the graph encodes statistical constraints. We will consider features of such models that can be determined from data under assumptions that are related to those routinely applied in experimental situations:
