An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks
Abstract
Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithm proposed by Wallace et al. [ 10 ] has demonstrated its ability in discovering Linear Causal Models from data. To explore the ways to improve efficiency, this research examines three different encoding schemes and four searching strategies. The experimental results reveal that (1) specifying parents encoding method is the best among three encoding methods we examined; (2) In the discovery of linear causal models, local Hill climbing works very well compared to other more sophisticated methods, like Markov Chain Monte Carto (MCMC), Genetic Algorithm (GA) and Parallel MCMC searching.
Cite
Text
Dai et al. "An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks." European Conference on Machine Learning, 2002. doi:10.1007/3-540-36755-1_5Markdown
[Dai et al. "An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks." European Conference on Machine Learning, 2002.](https://mlanthology.org/ecmlpkdd/2002/dai2002ecml-empirical/) doi:10.1007/3-540-36755-1_5BibTeX
@inproceedings{dai2002ecml-empirical,
title = {{An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks}},
author = {Dai, Honghua and Li, Gang and Tu, Yiqing},
booktitle = {European Conference on Machine Learning},
year = {2002},
pages = {48-59},
doi = {10.1007/3-540-36755-1_5},
url = {https://mlanthology.org/ecmlpkdd/2002/dai2002ecml-empirical/}
}