Causality
Causality in pyAgrum mainly consists in the ability to build a causal model, i.e. a (observational) Bayesian network and a set of latent variables and their relation with observation variables and in the abilidy to compute using do-calculus the causal impact in such a model.
Causality is a set of pure python3 scripts based on pyAgrum’s tools.
Note
In the figure above, pyAgrum.causal module can use a LaTeX special arrow (\(\hookrightarrow\)) to compactly represent an intervention. By default, it uses the classical “do” notation. You can change this behavior to anything you want by using the following configuration keys:
pyAgrum.config["causal","latex_do_prefix"]="\hookrightarrow("
pyAgrum.config["causal","latex_do_suffix"]=")"
Tutorials
Some implemented examples from the book of Why from Judea Pearl and Dana Mackenzie.
Reference
- Causal Model
CausalModelCausalModel.addCausalArc()CausalModel.addLatentVariable()CausalModel.arcs()CausalModel.backDoor()CausalModel.causalBN()CausalModel.children()CausalModel.connectedComponents()CausalModel.eraseCausalArc()CausalModel.existsArc()CausalModel.frontDoor()CausalModel.idFromName()CausalModel.latentVariablesIds()CausalModel.names()CausalModel.nodes()CausalModel.observationalBN()CausalModel.parents()CausalModel.toDot()
- Causal Formula
- Causal Inference
- Other functions
- Abstract Syntax Tree for Do-Calculus
- Exceptions
- Notebook’s tools for causality