Package com.bayesserver.causal

Interface Summary

Interface	Description
CausalQueryOutput	Additional outputs specific to causal queries.
Identification	Determines how to quantify a cause-effect relationship (for a particular criterion), but does not perform the actual estimation.
IdentificationOptions	Options for classes that implement Identification
IdentificationOutput	Output for classes that implement Identification
NodeSet	A set of nodes.
NodeSetItem	Represents a node in a set.
Validation	Methods to test whether adjustment inputs are valid.
ValidationOptions	Options for classes that implement Validation

Class Summary

Class	Description
Abduction	Performs abduction which is one of the steps in 'counterfactual analysis'.
AbductionOptions	Options for Abduction.update(com.bayesserver.inference.Evidence, java.util.List<com.bayesserver.Variable>, java.util.List<com.bayesserver.Variable>, com.bayesserver.causal.AbductionOptions).
AdjustmentSet	The set of nodes that an estimation procedure must adjust for (condition on) to avoid any bias in the results.
AdjustmentSetNode	Represents a node in an adjustment set.
BackdoorCriterion	Uses the 'Backdoor Criterion' to identify 'adjustment sets', that if found can be used to estimate the causal effect using theBackdoorInference.
BackdoorCriterionOptions	Options for BackdoorCriterion.
BackdoorCriterionOutput	The output from the Backdoor criterion, including any 'adjustment sets' identified.
BackdoorGraph	Methods for constructing the Backdoor graph or proper Backdoor graph from a Bayesian network.
BackdoorGraphOptions	Options for 'Backdoor graph' construction.
BackdoorInference	Estimates the causal effect, using the 'Backdoor Adjustment' formula to avoid confounding bias.
BackdoorInferenceFactory	Uses the factory design pattern to create inference related objects for the Backdoor adjustment algorithm.
BackdoorQueryOptions	Options for BackdoorInference
BackdoorQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
BackdoorValidationOptions	Options for Backdoor Criterion validation, which can be used to test whether adjustment sets are valid.
CausalInferenceBase	Base class for Causal inference engines used by internal algorithms.
CausalNode	Represents a reference to any node in a Causal model, for example a treatment (X), an outcome (Y), an unobserved node (U).
CausalQueryOptionsBase	Base class for causal query options.
CausalQueryOutputBase	Base class for causal algorithm output.
DisjunctiveCauseCriterion	Validates inputs for the Disjunctive cause adjustment.
DisjunctiveCauseCriterionOptions	Options for Disjunctive-cause Criterion validation.
DisjunctiveCauseCriterionOutput	The output from the Disjunctive-cause criterion, which is simply an adjustment set which includes all causes of treatments (X) or causes of outcomes (Y) or causes of both.
DisjunctiveCauseInference	Estimates the causal effect, using the 'Disjunctive Cause Criterion' adjustment formula to avoid confounding bias.
DisjunctiveCauseInferenceFactory	Uses the factory design pattern to create inference related objects for the Disjunctive cause algorithm.
DisjunctiveCauseQueryOptions	Options for DisjunctiveCauseInference.
DisjunctiveCauseQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
DisjunctiveCauseSet	Identifies sets of nodes used by the Disjunctive Cause Criterion algorithm.
DisjunctiveCauseSetNode	Represents a node in a set used by the Disjunctive Cause Criterion algorithm.
DisjunctiveCauseValidationOptions	Options for Disjunctive-cause criterion validation.
EffectsAnalysis	Calculates the causal effect on a target, varying for different treatment values.
EffectsAnalysisOptions	Options for an effects analysis.
EffectsAnalysisOutput	The results of an effects analysis.
EffectsAnalysisOutputItem	The result of an effects analysis for a particular treatment value.
FrontDoorCriterion	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.
FrontDoorCriterionOptions	Options for FrontDoorCriterion.
FrontDoorCriterionOutput	The output from the Front-door criterion, including any sets of 'front-door nodes' identified.
FrontDoorInference	Estimates the causal effect, using the 'Front-door Adjustment' formula to avoid confounding bias.
FrontDoorInferenceFactory	Uses the factory design pattern to create inference related objects for the Front-door adjustment algorithm.
FrontDoorQueryOptions	Options for FrontDoorInference
FrontDoorQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
FrontDoorSet	Front-door nodes used by the front-door adjustment.
FrontDoorSetNode	Represents a front-door node used by the front-door adjustment, and can be identified by the front-door criterion.
FrontDoorValidationOptions	Options for Front-door Criterion validation, which can be used to test whether the front-door nodes are valid and the pair of associated 'adjustment sets' are also valid..
IndirectGraph	Methods for constructing the 'Indirect graph' from a Bayesian network.
IndirectGraphOptions	Options for 'Indirect graph' construction.

Enum Summary

Enum	Description
BackdoorMethod	The sets for the Backdoor criterion to find.

Exception Summary

Exception	Description
AdjustmentNotFoundException	Raised by a causal inference algorithm when an adjustment set cannot be found.
ValidationException	Raised by an identification algorithm when validation fails.