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    AdjustmentSet

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    pub struct AdjustmentSet {
      adjustment_type : String
      adjustment_variables : Array[String]
    }

    The adjustment set that an identifier found.

    This is the port of dowhy.causal_identifier.adjustment_set.AdjustmentSet.

    AdjustmentSet::adjustment_type

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    fn AdjustmentSet::adjustment_type(self : AdjustmentSet) -> String

    The technique the adjustment set belongs to, "backdoor" or "general".

    AdjustmentSet::adjustment_variables

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    fn AdjustmentSet::adjustment_variables(self : AdjustmentSet) -> Array[String]

    The variables of the adjustment set.

    AdjustmentSet::new

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    fn AdjustmentSet::new(adjustment_type : String, adjustment_variables : Array[String]) -> AdjustmentSet

    A new adjustment set of the given type.

    CompleteTestCase

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    pub struct CompleteTestCase {
      nodes : Array[String]
      edges : Array[String]
      action_nodes : Array[String]
      outcome_nodes : Array[String]
      observed_nodes : Array[String]
      minimal_sets : Array[Array[String]]
    }

    A complete adjustment example graph.

    CompleteTestCase::new

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    fn CompleteTestCase::new(nodes : Array[String], edges : Array[String], action_nodes : Array[String], outcome_nodes : Array[String], observed_nodes : Array[String], minimal_sets : Array[Array[String]]) -> CompleteTestCase

    A new complete adjustment test case.

    FrontdoorTestCase

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    pub struct FrontdoorTestCase {
      nodes : Array[String]
      edges : Array[String]
      action_nodes : Array[String]
      outcome_nodes : Array[String]
      observed_nodes : Array[String]
      valid_sets : Array[Array[String]]
      invalid_sets : Array[Array[String]]
    }

    A frontdoor example graph with the valid and the invalid sets.

    FrontdoorTestCase::new

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    fn FrontdoorTestCase::new(nodes : Array[String], edges : Array[String], action_nodes : Array[String], outcome_nodes : Array[String], observed_nodes : Array[String], valid_sets : Array[Array[String]], invalid_sets : Array[Array[String]]) -> FrontdoorTestCase

    A new frontdoor test case.

    IdentifiedEstimand

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    pub struct IdentifiedEstimand {
      treatment_variable : Array[String]
      outcome_variable : Array[String]
      estimand_type : String
      estimands : Map[String, Array[String]]
      backdoor_variables : Map[String, Array[String]]
      general_adjustment_variables : Map[String, Array[String]]
      instrumental_variables : Array[String]
      frontdoor_variables : Array[String]
      mediator_variables : Array[String]
      mediation_first_stage_confounders : Map[String, Array[String]]
      mediation_second_stage_confounders : Map[String, Array[String]]
      default_backdoor_id : String
      default_adjustment_set_id : String
      identifier_method : String
      no_directed_path : Bool
    }

    The estimand that the identification step produced.

    This is the port of dowhy.causal_identifier.identified_estimand.

    An identified estimand carries the treatment and the outcome, the adjustment variables that the chosen identifier selected, the instruments, the frontdoor variables and the mediators. The estimators consume it and the refuters replace it, so it has to be cheap to copy.

    IdentifiedEstimand::copy

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    A copy of the estimand.

    The variable lists and the registries are copied as well, so the copy and the original share no state. This matters for the refuters: they replace the adjustment set of a copy, and the estimator they were handed has to keep the adjustment set it was fitted with.

    IdentifiedEstimand::default_backdoor_id

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    fn IdentifiedEstimand::default_backdoor_id(self : IdentifiedEstimand) -> String

    The identifier of the default backdoor set, e.g. "backdoor1".

    IdentifiedEstimand::estimand_assumptions

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    fn IdentifiedEstimand::estimand_assumptions(self : IdentifiedEstimand, key : String) -> Array[String]

    The assumptions of an estimand in the registry.

    The registry stores the assumption names of every estimand; the strings match the ones dowhy reports.

    IdentifiedEstimand::estimand_keys

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    fn IdentifiedEstimand::estimand_keys(self : IdentifiedEstimand) -> Array[String]

    The names of the estimands in the registry.

    IdentifiedEstimand::estimand_type

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    fn IdentifiedEstimand::estimand_type(self : IdentifiedEstimand) -> String

    The estimand type, e.g. "nonparametric-ate".

    IdentifiedEstimand::get_adjustment_set

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    fn IdentifiedEstimand::get_adjustment_set(self : IdentifiedEstimand, key : String) -> Array[String]

    The adjustment variables of the current identifier.

    Mirrors IdentifiedEstimand.get_adjustment_set: general adjustment sets are used when the identifier is "general_adjustment", otherwise the backdoor variables are.

    IdentifiedEstimand::get_backdoor_variables

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    fn IdentifiedEstimand::get_backdoor_variables(self : IdentifiedEstimand, key : String) -> Array[String]

    The backdoor variables of the current identifier.

    IdentifiedEstimand::get_frontdoor_variables

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    fn IdentifiedEstimand::get_frontdoor_variables(self : IdentifiedEstimand) -> Array[String]

    The frontdoor variables of the estimand.

    IdentifiedEstimand::get_general_adjustment_variables

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    fn IdentifiedEstimand::get_general_adjustment_variables(self : IdentifiedEstimand, key : String) -> Array[String]

    The adjustment variables of the complete adjustment criterion.

    IdentifiedEstimand::get_instrumental_variables

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    fn IdentifiedEstimand::get_instrumental_variables(self : IdentifiedEstimand) -> Array[String]

    The instruments of the estimand.

    IdentifiedEstimand::get_mediator_variables

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    fn IdentifiedEstimand::get_mediator_variables(self : IdentifiedEstimand) -> Array[String]

    The mediator variables of the estimand.

    IdentifiedEstimand::has_estimand

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    fn IdentifiedEstimand::has_estimand(self : IdentifiedEstimand, key : String) -> Bool

    Whether the registry contains the given estimand.

    IdentifiedEstimand::has_no_directed_path

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    fn IdentifiedEstimand::has_no_directed_path(self : IdentifiedEstimand) -> Bool

    Whether the causal graph has no directed path from treatment to outcome.

    IdentifiedEstimand::identifier_method

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    fn IdentifiedEstimand::identifier_method(self : IdentifiedEstimand) -> String

    The name of the identifier that produced the estimand.

    IdentifiedEstimand::new

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    fn IdentifiedEstimand::new(treatment_variable : Array[String], outcome_variable : Array[String]) -> IdentifiedEstimand

    A fresh estimand for a treatment and an outcome.

    IdentifiedEstimand::outcome

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    fn IdentifiedEstimand::outcome(self : IdentifiedEstimand) -> Array[String]

    The outcome variables.

    IdentifiedEstimand::set_adjustment_set

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    fn IdentifiedEstimand::set_adjustment_set(self : IdentifiedEstimand, variables : Array[String], key : String) -> Unit

    Replace the adjustment variables of the current identifier.

    IdentifiedEstimand::set_default_adjustment_set_id

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    fn IdentifiedEstimand::set_default_adjustment_set_id(self : IdentifiedEstimand, id : String) -> Unit

    Set the identifier of the default general adjustment set.

    IdentifiedEstimand::set_default_backdoor_id

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    fn IdentifiedEstimand::set_default_backdoor_id(self : IdentifiedEstimand, id : String) -> Unit

    Set the identifier of the default backdoor set.

    IdentifiedEstimand::set_estimand

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    fn IdentifiedEstimand::set_estimand(self : IdentifiedEstimand, key : String, assumptions : Array[String]) -> Unit

    Register an estimand with its assumptions.

    IdentifiedEstimand::set_estimand_type

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    fn IdentifiedEstimand::set_estimand_type(self : IdentifiedEstimand, estimand_type : String) -> Unit

    Set the type of the estimand, e.g. "nonparametric-ate".

    IdentifiedEstimand::set_frontdoor_variables

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    fn IdentifiedEstimand::set_frontdoor_variables(self : IdentifiedEstimand, variables : Array[String]) -> Unit

    Set the frontdoor variables of the estimand.

    IdentifiedEstimand::set_general_adjustment_variables

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    fn IdentifiedEstimand::set_general_adjustment_variables(self : IdentifiedEstimand, variables : Array[String], key : String) -> Unit

    Set the adjustment variables of the complete adjustment criterion.

    IdentifiedEstimand::set_identifier_method

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    fn IdentifiedEstimand::set_identifier_method(self : IdentifiedEstimand, name : String) -> Unit

    Set the name of the identifier that produced the estimand.

    IdentifiedEstimand::set_instrumental_variables

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    fn IdentifiedEstimand::set_instrumental_variables(self : IdentifiedEstimand, variables : Array[String]) -> Unit

    Set the instruments of the estimand.

    IdentifiedEstimand::set_no_directed_path

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    fn IdentifiedEstimand::set_no_directed_path(self : IdentifiedEstimand, flag : Bool) -> Unit

    Set whether the causal graph has no directed path from treatment to outcome.

    IdentifiedEstimand::treatment

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    fn IdentifiedEstimand::treatment(self : IdentifiedEstimand) -> Array[String]

    The treatment variables.

    IdentifiedEstimand::with_adjustment_variables

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    fn IdentifiedEstimand::with_adjustment_variables(self : IdentifiedEstimand, variables : Array[String]) -> IdentifiedEstimand

    A copy of the estimand with a different adjustment set.

    The random common cause refuter uses this to add the noise variable to the adjustment set.

    IdentifiedEstimand::with_no_directed_path

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    fn IdentifiedEstimand::with_no_directed_path(self : IdentifiedEstimand, flag : Bool) -> IdentifiedEstimand

    A copy of the estimand with the "no directed path" flag set.

    The identification returns such an estimand when the causal graph has no directed path from the treatment to the outcome.

    IdentifierTestCase

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    pub struct IdentifierTestCase {
      nodes : Array[String]
      edges : Array[String]
      action_nodes : Array[String]
      outcome_nodes : Array[String]
      observed_nodes : Array[String]
      biased_sets : Array[Array[String]]
      minimal_sets : Array[Array[String]]
      maximal_sets : Array[Array[String]]
      direct_maximal_sets : Array[Array[String]]
    }

    One example graph with the adjustment sets it has to produce.

    IdentifierTestCase::new

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    fn IdentifierTestCase::new(nodes : Array[String], edges : Array[String], action_nodes : Array[String], outcome_nodes : Array[String], observed_nodes : Array[String], biased_sets : Array[Array[String]], minimal_sets : Array[Array[String]], maximal_sets : Array[Array[String]], direct_maximal_sets : Array[Array[String]]) -> IdentifierTestCase

    A new test case.

    BACKDOOR_DEFAULT

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    let BACKDOOR_DEFAULT : String

    The default backdoor strategy: the maximal and the minimal set.

    BACKDOOR_EFFICIENT

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    let BACKDOOR_EFFICIENT : String

    The efficient backdoor strategy of Smucler, Sapienza and Rotnitzky.

    BACKDOOR_EXHAUSTIVE

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    let BACKDOOR_EXHAUSTIVE : String

    The exhaustive backdoor strategy: every valid set.

    BACKDOOR_MAX

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    let BACKDOOR_MAX : String

    The maximal backdoor strategy: the largest valid set.

    BACKDOOR_MIN

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    let BACKDOOR_MIN : String

    The minimal backdoor strategy: the smallest valid set.

    BACKDOOR_MINCOST_EFFICIENT

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    let BACKDOOR_MINCOST_EFFICIENT : String

    The efficient minimum cost backdoor strategy.

    BACKDOOR_MIN_EFFICIENT

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    let BACKDOOR_MIN_EFFICIENT : String

    The efficient minimal backdoor strategy.

    EFFICIENT_BACKDOOR_NOT_IMPLEMENTED

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    let EFFICIENT_BACKDOOR_NOT_IMPLEMENTED : String

    The message that the efficient backdoor search is not implemented yet.

    backdoor_adjustment_error

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    fn backdoor_adjustment_error(backdoor_adjustment : String) -> String

    The error message of a backdoor strategy, or an empty string when the strategy is supported.

    backdoor_cases

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    fn backdoor_cases() -> Array[IdentifierTestCase]

    Every backdoor example graph of the upstream test suite.

    complete_cases

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    fn complete_cases() -> Array[CompleteTestCase]

    Every complete adjustment example graph of the upstream test suite.

    frontdoor_cases

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    fn frontdoor_cases() -> Array[FrontdoorTestCase]

    Every frontdoor example graph of the upstream test suite.

    identify_backdoor

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    fn identify_backdoor(graph : 
    DirectedGraph    , action_nodes : Array[String], outcome_nodes : Array[String], observed_nodes : Array[String], backdoor_adjustment : String, direct_effect? : Bool) -> (Array[AdjustmentSet], String)

    The backdoor adjustment sets of the graph.

    The first component holds the adjustment sets, the second one an error message that is empty when the search succeeded. The three "efficient" strategies are not implemented yet: they return an empty list of adjustment sets and a message that says so, rather than a wrong set.

    The candidate sets are enumerated in sorted order. The Python code iterates over a set of variables, so the order in which it finds the valid sets depends on the hash of the variable names and changes between runs; the sorted order makes the result reproducible.

    identify_effect

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    fn identify_effect(graph : 
    DirectedGraph    , action_nodes : Array[String], outcome_nodes : Array[String], observed_nodes : Array[String]) -> IdentifiedEstimand

    The identified estimand of the causal effect of the treatment.

    This is the same as identify_effect_auto with the default backdoor strategy and the average total effect.

    identify_effect_auto

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    fn identify_effect_auto(graph : 
    DirectedGraph    , action_nodes : Array[String], outcome_nodes : Array[String], observed_nodes : Array[String], estimand_type : String, backdoor_adjustment : String) -> IdentifiedEstimand

    The identified estimand of the causal effect of the treatment.

    The estimand carries the adjustment sets of the backdoor criterion, the instruments and the frontdoor variables, together with the assumptions that every one of them needs.

    identify_frontdoor

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    fn identify_frontdoor(graph : 
    DirectedGraph    , action_nodes : Array[String], outcome_nodes : Array[String], observed_nodes : Array[String]) -> Array[String]

    The frontdoor variables of the graph.

    The result is the smallest set that satisfies the three conditions of the frontdoor criterion; it is empty when no such set exists. The Python code keeps the last set it finds, which is the largest one because of the break that only leaves the inner loop; the smallest set is the one the frontdoor criterion asks for.

    identify_generalized_adjustment_set

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    fn identify_generalized_adjustment_set(graph : 
    DirectedGraph    , action_nodes : Array[String], outcome_nodes : Array[String], observed_nodes : Array[String]) -> Array[AdjustmentSet]

    The adjustment set of the complete adjustment criterion.

    The complete criterion of Shpitser et al. finds an adjustment set that is not a backdoor set. The set is a minimal d-separator of the treatment and the outcome in the proper backdoor graph, restricted to the observed variables that are not descendants of a proper causal path node.

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