Package com.bayesserver.causal

Class FrontDoorCriterion

java.lang.Object

com.bayesserver.causal.FrontDoorCriterion

All Implemented Interfaces:

Identification, Validation

public final class FrontDoorCriterion
extends Object
implements Identification, Validation

Uses the 'Front-door Criterion' to identify any sets of valid front-door nodes, that if found can be used to estimate the causal effect using theFrontDoorInference.

Although the estimation can also be performed using Bayes Server, this criterion can also be used to identify front-door node sets for use outside Bayes Server.

Constructor Summary

Constructors

Constructor	Description
FrontDoorCriterion(Network network)	Initializes a new instance of the FrontDoorCriterion class.

Method Summary

Modifier and Type	Method	Description
Network	getNetwork()	The Bayesian network on which the identification is based.
IdentificationOutput	identify(Evidence evidence, Distribution query, IdentificationOptions options)	Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
IdentificationOutput	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	identifyXZ(Evidence evidence, FrontDoorSet frontDoorNodes, BackdoorCriterionOptions options)	Uses the 'Backdoor criterion' to identify any 'adjustment sets' between treatments (X) and front-door nodes (Z).
BackdoorCriterionOutput	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	identifyZY(FrontDoorSet frontDoorNodes, List<CausalNode> outcomes, List<CausalNode> nonTreatmentEvidence, BackdoorCriterionOptions options)
BackdoorCriterionOutput	identifyZY(Evidence evidence, FrontDoorSet frontDoorNodes, Distribution query, BackdoorCriterionOptions options)	Uses the 'Backdoor criterion' to identify any 'adjustment sets' between front-door nodes (Z) and outcomes (Y).
boolean	isValid(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, without raising an exception.
boolean	isValid(List<CausalNode> treatments, List<CausalNode> outcomes, List<CausalNode> nonTreatmentEvidence, ValidationOptions options)	Tests whether adjustment inputs are valid, without raising an exception.
void	validate(Evidence evidence, Distribution query, ValidationOptions options)	Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.
void	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.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FrontDoorCriterion

public FrontDoorCriterion(Network network)

Initializes a new instance of the FrontDoorCriterion class.

Parameters:

network - The Bayesian network.

Method Detail

identifyXZ

public BackdoorCriterionOutput identifyXZ(Evidence evidence,
                                          FrontDoorSet frontDoorNodes,
                                          BackdoorCriterionOptions options)

Uses the 'Backdoor criterion' to identify any 'adjustment sets' between treatments (X) and front-door nodes (Z).

Parameters:

evidence - Evidence which contains both one or more interventions (treatments X), and optionally other standard (non-intervention) evidence (adjusted nodes)).

frontDoorNodes - One or more front-door nodes (Z).

options - Options for the Backdoor criterion.

Returns:

A list of possibly empty adjustment sets. i

identifyXZ

public BackdoorCriterionOutput 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).

Parameters:

treatments - One or more interventions (treatments X).

frontDoorNodes - One or more front-door nodes (Z).

nonTreatmentEvidence - Any other evidence other than interventions.

options - Options for the Backdoor criterion.

Returns:

A list of possibly empty adjustment sets. i

identifyZY

public BackdoorCriterionOutput identifyZY(Evidence evidence,
                                          FrontDoorSet frontDoorNodes,
                                          Distribution query,
                                          BackdoorCriterionOptions options)

Uses the 'Backdoor criterion' to identify any 'adjustment sets' between front-door nodes (Z) and outcomes (Y).

Parameters:

evidence - Evidence which contains both one or more interventions (treatments X), and optionally other standard (non-intervention) evidence (adjusted nodes)).

frontDoorNodes - One or more front-door nodes (Z).

query - One or more outcomes (Y).

options - Options for the Backdoor criterion.

Returns:

A list of possibly empty adjustment sets.

identifyZY

public BackdoorCriterionOutput identifyZY(FrontDoorSet frontDoorNodes,
                                          List<CausalNode> outcomes,
                                          List<CausalNode> nonTreatmentEvidence,
                                          BackdoorCriterionOptions options)

Parameters:

frontDoorNodes - One or more front-door nodes (Z).

outcomes - One or more outcomes (Y).

nonTreatmentEvidence - Any other evidence other than interventions.

options - Options for the Backdoor criterion.

Returns:

A list of possibly empty adjustment sets.

identify

public IdentificationOutput identify(Evidence evidence,
                                     Distribution query,
                                     IdentificationOptions options)

Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.

Specified by:

identify in interface Identification

Parameters:

evidence - Evidence which contains both one or more interventions (treatments X), and optionally other standard (non-intervention) evidence.

query - One or more outcomes (effects), often denoted Y.

options - Other options that govern how the calculations are performed. Some options are common to all criterion, and some will have additional options specific to that criterion.

Returns:

Information on how to calculate the requested cause-effect relationship.

getNetwork

public Network getNetwork()

The Bayesian network on which the identification is based.

Specified by:

getNetwork in interface Identification

isValid

public boolean isValid(Evidence evidence,
                       Distribution query,
                       ValidationOptions options)

Tests whether adjustment inputs are valid, without raising an exception. If invalid, you can also call com.bayesserver.causal.Validation#validate(IEvidence, IDistribution, IValidationOptions) 'Validate' to throw an exception which contains an error message.

Specified by:

isValid in interface Validation

Parameters:

evidence - Evidence which contains both one or more interventions (treatments X), and optionally other standard (non-intervention) evidence.

query - One or more outcomes (effects) Y.

options - Other options that govern how the calculations are performed. Some options are common to all criterion, and some will have additional options specific to that criterion.

Returns:

true if valid, false otherwise.

validate

public void validate(Evidence evidence,
                     Distribution query,
                     ValidationOptions options)

Tests whether adjustment inputs are valid, and throws an exception if not, with an error message.

Specified by:

validate in interface Validation

Parameters:

evidence - Evidence which contains both one or more interventions (treatments X), and optionally other standard (non-intervention) evidence.

query - One or more outcomes (effects) Y.

options - Other options that govern how the calculations are performed. Some options are common to all criterion, and some will have additional options specific to that criterion.

identify

public IdentificationOutput 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.

Specified by:

identify in interface Identification

Parameters:

treatments - One or more treatments/interventions (X).

outcomes - One or more outcomes (effects), often denoted Y.

nonTreatmentEvidence - Any other evidence other than interventions.

options - Other options that govern how the calculations are performed. Some options are common to all criterion, and some will have additional options specific to that criterion.

Returns:

Information on how to calculate the requested cause-effect relationship.

validate

public void 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.

Specified by:

validate in interface Validation

Parameters:

treatments - One or more treatments/interventions (X).

outcomes - One or more outcomes (effects), often denoted Y.

nonTreatmentEvidence - Any non-treatment evidence.

options - Other options that govern how the calculations are performed. Some options are common to all criterion, and some will have additional options specific to that criterion.

isValid

public boolean isValid(List<CausalNode> treatments,
                       List<CausalNode> outcomes,
                       List<CausalNode> nonTreatmentEvidence,
                       ValidationOptions options)

Tests whether adjustment inputs are valid, without raising an exception. If invalid, you can also call {@link com.bayesserver.causal.Validation#validate(IList{CausalNode}, IList{CausalNode}, IList{CausalNode}, IValidationOptions)} 'Validate' to throw an exception which contains an error message.

Specified by:

isValid in interface Validation

Parameters:

treatments - One or more treatments/interventions (X).

outcomes - One or more outcomes (effects), often denoted Y.

nonTreatmentEvidence - Any non-treatment evidence.

options - Other options that govern how the calculations are performed. Some options are common to all criterion, and some will have additional options specific to that criterion.

Returns:

true if valid, false otherwise.