Generate the partially directed acyclic graph representing the equivalence class of a Bayesian network learned using interventions.
ctsdag(x, exp, learning = FALSE, debug = FALSE)an object of class bn, the network from which to compute
the PDAG.
a vector of character strings, the labels of the node that are the
targets of the interventions. If no targets are provided, ctsdag()
just reverts to cpdag().
a boolean value. If TRUE, interventions, whitelists
and blacklists used in learning the structure of x will be taken
into account in contructing the PDAG. These interventions will be applied
in addition to those provided via the exp argument.
a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent.
ctsdag returns an object of class bn, representing the
equivalence class. See bn-class for details.
ctsdag() extends cpdag() by incorporating interventions in
constructing the partially directed acyclic graph that represents the
equivalence class of x; it preserves the directions of arcs that
are compelled because they are incident on the target nodes specified by the
exp argument. This assumes do-calculus model of targeted
interventions with no unknown side-effects.
It also takes into account prior arc probabilities used in structure learning,
ensuring that DAGs are equivalent in posterior probability only if they are
equivalent in prior probability. This is not the case for graph priors other
than the uniform (uniform) and marginal uniform priors
(marginal, see bn-class for details).
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Chickering DM (1995). "A Transformational Characterization of Equivalent Bayesian Network Structures". Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, pp. 87--98.
Ness RO, Sachs K, Mallick P, Vitek O (2017). "A Bayesian Active Learning Experimental Design for Inferring Signaling Networks". International Conference on Research in Computational Molecular Biology, pp. 134--156.
Tian J, Pearl J (2001). "Causal Discovery from Changes". Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 512--521.