Causal Model

class pyAgrum.causal.CausalModel(bn: pyAgrum.BayesNet, latentVarsDescriptor: Optional[List[Tuple[str, Tuple[str, str]]]] = None, keepArcs: bool = False)

From an observational BNs and the description of latent variables, this class represent a complet causal model obtained by adding the latent variables specified in latentVarsDescriptor to the Bayesian network bn.

Parameters:

• bn – a observational bayesian network
• latentVarsDescriptor – list of couples (<latent variable name>, <list of affected variables’ ids>).
• keepArcs – By default, the arcs between variables affected by a common latent variable will be removed but this can be avoided by setting keepArcs to True

causalBN() → pyAgrum.BayesNet

Returns:the causal Bayesian network Warning:do not infer any computations in this model. It is strictly a structural model

children(x: Union[int, str]) → Set[int]

Parameters:x – the node Returns:

idFromName(name: str) → int

Parameters:name – the name of the variable Returns:the id of the variable

latentVariablesIds() → Set[int]

Returns:the set of ids of latent variables in the causal model

names() → Dict[int, str]

Returns:the map NodeId,Name

observationalBN() → pyAgrum.BayesNet

Returns:the observational Bayesian network

parents(x: Union[int, str]) → Set[int]

From a NodeId, returns its parent (as a set of NodeId)

Parameters:x – the node Returns: