Intervals of Causal Effects for Learning Causal Graphical Models.
Samuel Antonio Montero-HernándezFelipe Orihuela-EspinaLuis Enrique SucarPublished in: PGM (2018)
Keyphrases
- graphical models
- bayesian networks
- causal bayesian networks
- belief propagation
- reinforcement learning
- probabilistic graphical models
- learning algorithm
- causal effects
- approximate inference
- relational dependency networks
- markov logic networks
- probabilistic inference
- probabilistic model
- conditional independence
- causal relations
- undirected graphical models
- structural learning
- random variables
- structure learning
- conditional random fields
- statistical relational learning
- supervised learning