Causal Learning From Predictive Modeling for Observational Data.
Front Big Data
; 3: 535976, 2020.
Article
en En
| MEDLINE
| ID: mdl-33693412
ABSTRACT
We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal relationships among model variables. To this effect, we explore the use of two types of independencies-context-specific independence (CSI) and mutual independence (MI). We use CSI to identify the candidate set of causal relationships and then use MI to quantify their strengths and construct a causal model. We validate the learned models on benchmark networks and demonstrate the effectiveness when compared to some of the state-of-the-art Causal Bayesian Network Learning algorithms from observational Data.
Texto completo:
1
Banco de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Front Big Data
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos