static void |
BackdoorGraph.convert(Network network,
List<CausalNode> treatments,
List<CausalNode> outcomes,
BackdoorGraphOptions options) |
Constructs the Backdoor graph or the proper Backdoor graph from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
|
static void |
IndirectGraph.convert(Network network,
List<CausalNode> treatments,
List<CausalNode> outcomes,
IndirectGraphOptions options) |
Constructs the 'Indirect graph' from a Bayesian network, one of more treatments (X) and one or more outcomes (Y).
|
IdentificationOutput |
BackdoorCriterion.identify(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
IdentificationOptions options) |
Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
|
IdentificationOutput |
DisjunctiveCauseCriterion.identify(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
IdentificationOptions options) |
Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
|
IdentificationOutput |
FrontDoorCriterion.identify(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
IdentificationOptions options) |
Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
|
IdentificationOutput |
Identification.identify(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
IdentificationOptions options) |
Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
|
BackdoorCriterionOutput |
FrontDoorCriterion.identifyXZ(List<CausalNode> treatments,
FrontDoorSet frontDoorNodes,
List<CausalNode> nonTreatmentEvidence,
BackdoorCriterionOptions options) |
Uses the 'Backdoor criterion' to identify any 'adjustment sets' between treatments (X) and front-door nodes (Z).
|
BackdoorCriterionOutput |
FrontDoorCriterion.identifyZY(FrontDoorSet frontDoorNodes,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
BackdoorCriterionOptions options) |
|
boolean |
BackdoorCriterion.isValid(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
ValidationOptions options) |
Tests whether adjustment inputs are valid, without raising an exception.
|
boolean |
DisjunctiveCauseCriterion.isValid(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> adjusted,
ValidationOptions options) |
Tests whether adjustment inputs are valid, without raising an exception.
|
boolean |
FrontDoorCriterion.isValid(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
ValidationOptions options) |
Tests whether adjustment inputs are valid, without raising an exception.
|
boolean |
Validation.isValid(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
ValidationOptions options) |
Tests whether adjustment inputs are valid, without raising an exception.
|
void |
BackdoorCriterion.validate(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
ValidationOptions options) |
Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
|
void |
DisjunctiveCauseCriterion.validate(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
ValidationOptions options) |
Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
|
void |
FrontDoorCriterion.validate(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
ValidationOptions options) |
Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
|
void |
Validation.validate(List<CausalNode> treatments,
List<CausalNode> outcomes,
List<CausalNode> nonTreatmentEvidence,
ValidationOptions options) |
Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
|