Indirect Causes in Dynamic Bayesian Networks Revisited
Abstract
Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a "causal design," i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By introducing activator random variables, we propose template fragments for modeling dynamic Bayesian networks under a causal use of time, anticipating indirect influences on a solid mathematical basis, obeying the laws of Bayesian networks.
Cite
Text
Motzek and Möller. "Indirect Causes in Dynamic Bayesian Networks Revisited." Journal of Artificial Intelligence Research, 2017. doi:10.1613/JAIR.5361Markdown
[Motzek and Möller. "Indirect Causes in Dynamic Bayesian Networks Revisited." Journal of Artificial Intelligence Research, 2017.](https://mlanthology.org/jair/2017/motzek2017jair-indirect/) doi:10.1613/JAIR.5361BibTeX
@article{motzek2017jair-indirect,
title = {{Indirect Causes in Dynamic Bayesian Networks Revisited}},
author = {Motzek, Alexander and Möller, Ralf},
journal = {Journal of Artificial Intelligence Research},
year = {2017},
pages = {1-58},
doi = {10.1613/JAIR.5361},
volume = {59},
url = {https://mlanthology.org/jair/2017/motzek2017jair-indirect/}
}