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Found 6 papers, 0 papers with code
Open problems in causal structure learning: A case study of COVID-19 in the UK
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships.
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions
The algorithm assumes causal insufficiency in the presence of latent variables and produces a Partial Ancestral Graph (PAG).
Effective and efficient structure learning with pruning and model averaging strategies
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function.
A survey of Bayesian Network structure learning
This paper provides a comprehensive review of combinatoric algorithms proposed for learning BN structure from data, describing 74 algorithms including prototypical, well-established and state-of-the-art approaches.
The impact of prior knowledge on causal structure learning
Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data.
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data
This paper investigates the performance of 15 structure learning algorithms.
