Causal Formula¶
CausalFormula is the class that represents a causal query in a causal model. Mainly it consits in
a reference to the CausalModel
Three sets of variables name that represent the 3 sets of variable in the query P(set1 | doing(set2),knowing(set3)).
the AST for compute the query.
- class pyAgrum.causal.CausalFormula(cm, root, on, doing, knowing=None)¶
Represents a causal query in a causal model. The query is encoded as an CausalFormula that can be evaluated in the causal model : $P(on|knowing, overhook (doing))$
- Parameters
cm (CausalModel) – the causal model
root (ASTtree) – the syntax tree
on (str|Set[str]) – the variable or the set of variables of interest
doing (str|Set[str]) – the intervention variable(s)
knowing (None|str|Set[str]) – the observation variable(s)
- property cm: CausalModel¶
- Returns
the causal model
- Return type
- copy()¶
Copy theAST. Note that the causal model is just referenced. The tree is copied.
- Returns
the copu
- Return type
- eval()¶
Compute the Potential from the CausalFormula over vars using cond as value for others variables
- Returns
The resulting distribution
- Return type
- latexQuery(values=None)¶
Returns a string representing the query compiled by this Formula. If values, the query is annotated with the values in the dictionary.
- Parameters
values (None|Dict[str,str]) – the values to add in the query representation
- Returns
the LaTeX representation of the causal query for this CausalFormula
- Return type
str
- toLatex()¶
- Returns
a LaTeX representation of the CausalFormula
- Return type
str
Causal Inference¶
Obtaining and evaluating a CausalFormula is done using one these functions :
- pyAgrum.causal.causalImpact(cm, on, doing, knowing=None, values=None)¶
Determines the causal impact of interventions.
Determines the causal impact of the interventions specified in
doing
on the single or list of variableson
knowing the states of the variables inknowing
(optional). These last parameters is dictionary <variable name>:<value>. The causal impact is determined in the causal DAGcm
. This function returns a triplet with a latex format formula used to compute the causal impact, a potential representing the probability distribution ofon
given the interventions and observations as parameters, and an explanation of the method allowing the identification. If there is no impact, the joint probability ofon
is simply returned. If the impact is not identifiable the formula and the adjustment will beNone
but an explanation is still given.- Parameters
cm (CausalModel) – the causal model
on (str|NameSet) – variable name or variable names set of interest
doing (str|NameSet) – the interventions
knowing (str|NameSet) – the observations
values (Dict[str,int] default=None) – the values of interventions and observations
- Returns
the CausalFormula, the computation, the explanation
- Return type
Tuple[CausalFormula,pyAgrum.Potential,str]
- pyAgrum.causal.doCalculusWithObservation(cm, on, doing, knowing=None)¶
Compute the CausalFormula for an impact analysis given the causal model, the observed variables and the variable on which there will be intervention.
- Parameters
cm (CausalModel) – the causal model
on (Set[str]) – the variables of interest
doing (Set[str]) – the interventions
knowing (Set[str] default=None) – the observations
- Returns
if possible, returns the formula to compute this intervention
- Return type
- Raises
HedgeException, UnidentifiableException – if this calculous is not possible
- pyAgrum.causal.identifyingIntervention(cm, Y, X, P=None)¶
Following Shpitser, Ilya and Judea Pearl. ‘Identification of Conditional Interventional Distributions.’ UAI2006 and ‘Complete Identification Methods for the Causal Hierarchy’ JMLR 2008
- Parameters
cm (
CausalModel
) – the causal modelY (
Set
[str
]) – The variables of interest (named following the paper)X (
Set
[str
]) – The variable of intervention (named following the paper)P (
Optional
[ASTtree
]) – The ASTtree representing the calculus in construction
- Return type
- Returns
the ASTtree representing the calculus
Other functions¶
- pyAgrum.causal.backdoor_generator(bn, cause, effect, not_bd=None)¶
Generates backdoor sets for the pair of nodes (cause, effect) in the graph bn excluding the nodes in the set not_bd (optional)
- Parameters
bn (pyAgrum.BayesNet) –
cause (int) –
effect (int) –
not_bd (Set[int] default=None) –
- Yields
List[int] – the different backdoors
- pyAgrum.causal.frontdoor_generator(bn, x, y, not_fd=None)¶
Generates frontdoor sets for the pair of nodes (x, y) in the graph bn excluding the nodes in the set not_fd (optional)
- Parameters
bn (pyAgrum.BayesNet) –
x (int) –
y (int) –
not_fd (Set[int] default=None) –
- Yields
List[int] – the different frontdoors