The Evolution of Causal Models: A Comparison of Bayesian Metrics and Structure Priors
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 @InProceedings{Neil:1999:ECM,

author = "Julian R. Neil and Kevin B. Korb",

title = "The Evolution of Causal Models: {A} Comparison of
{Bayesian} Metrics and Structure Priors",

booktitle = "Proceedings of the 3rd PacificAsia Conference on
Methodologies for Knowledge Discovery and Data Mining
({PAKDD}99)",

year = "1999",

editor = "Ning Zhong and Lizhu Zhou",

volume = "1574",

series = "Lecture Notes in Artificial Intelligence",

pages = "433438",

language = "English",

address = "Beijing, China",

month = "2628 " # apr,

publisher = "SpringerVerlag",

keywords = "genetic algorithms, genetic programming",

ISBN = "3540658661",

DOI = "doi:10.1007/3540489126_57",

size = "6 pages",

abstract = "We report the use genetic algorithms (GAs) as a search
mechanism for the discovery of linear causal models
when using two Bayesian metrics for linear causal
models, a Minimum Message Length (MML) metric [10] and
a full posterior analysis (BGe) [3]. We also consider
two structure priors over causal models, one giving all
variable orderings for models with the same arc density
equal prior probability (P1) and one assigning all
causal structures with the same arc density equal
priors (P2). Evaluated with KullbackLeibler distance
prior P2 tended to produce models closer to the true
model than P1 for both metrics, with MML performing
slightly better than BGe. By contrast, when using an
evaluation metric that better reflects the nature of
the causal discovery task, namely a metric that
compares the results of predictive performance on the
effect nodes in the discovered model P1 outperformed P2
in general, with MML and BGe discovering models of
similar predictive performance at various sample sizes.
This supports our conjecture that the P1 prior is more
appropriate for causal discovery.",

notes = "p436 'For this study we employed a nonstandard
genetic algorithm (cf. [5]), using DAGs directly as the
genetic representation, with genetic operators designed
for DAGs. These operators are described in detail in
[5] and are extended in [6].'
See also \cite{Neil96}",
 }
Genetic Programming entries for
Julian R Neil
Kevin B Korb
Citations