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Identifiability and transportability in dynamic causal networks

Autor
Blondel, G.; Arias, M.; Gavaldà, R.
Tipus d'activitat
Article en revista
Revista
International journal of data science and analytics
Data de publicació
2017-03
Volum
3
Número
2
Pàgina inicial
131
Pàgina final
147
DOI
https://doi.org/10.1007/s41060-016-0028-8 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/114969 Obrir en finestra nova
https://arxiv.org/abs/1610.05556v1 Obrir en finestra nova
URL
https://link.springer.com/article/10.1007/s41060-016-0028-8 Obrir en finestra nova
Resum
In this paper, we propose a causal analog to the purely observational dynamic Bayesian networks, which we call dynamic causal networks. We provide a sound and complete algorithm for the identification of causal effects in dynamic causal networks, namely for computing the effect of an intervention or experiment given a dynamic causal network and probability distributions of passive observations of its variables, whenever possible. We note the existence of two types of hidden confounder variables ...
Citació
Blondel, G., Arias, M., Gavaldà, R. Identifiability and transportability in dynamic causal networks. "International journal of data science and analytics", Març 2017, vol. 3, núm. 2, p. 131-147.
Paraules clau
Causal analysis, Confounding, Do-calculus, Dynamic modeling, Graphical models
Grup de recerca
LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge

Participants

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