Notebook’s tools for causality

This file defines some helpers for handling causal concepts in notebooks

pyAgrum.causal.notebook.getCausalImpact(model: pyAgrum.causal._CausalModel.CausalModel, on: Union[str, Set[str]], doing: Union[str, Set[str]], knowing: Optional[Set[str]] = None, values: Optional[Dict[str, int]] = None) → Tuple[str, pyAgrum.pyAgrum.Potential, str]

return a HTML representing of the three values defining a causal impact : formula, value, explanation :param model: the causal model :param on: the impacted variable(s) :param doing: the variable(s) of intervention :param knowing: the variable(s) of evidence :param values : values for certain variables

Returns:a triplet (CausalFormula, gum.Potential, explanation)
pyAgrum.causal.notebook.getCausalModel(cm: pyAgrum.causal._CausalModel.CausalModel, size: str = '4') → str

return a HTML representing the causal model :param cm: the causal model :param size: passd :param vals: :return:

pyAgrum.causal.notebook.showCausalImpact(model: pyAgrum.causal._CausalModel.CausalModel, on: Union[str, Set[str]], doing: Union[str, Set[str]], knowing: Optional[Set[str]] = None, values: Optional[Dict[str, int]] = None)

display a HTML representing of the three values defining a causal impact : formula, value, explanation :param model: the causal model :param on: the impacted variable(s) :param doing: the variable(s) of intervention :param knowing: the variable(s) of evidence :param values : values for certain variables

pyAgrum.causal.notebook.showCausalModel(cm: pyAgrum.causal._CausalModel.CausalModel, size: str = '4')

Shows a graphviz svg representation of the causal DAG d