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    CausalRefutation

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    pub struct CausalRefutation {
      estimated_effect : Double
      new_effect : Double
      new_effect_lower : Double
      new_effect_upper : Double
      refutation_type : String
      p_value : Double
      is_significant : Bool
    }

    The result of one refutation.

    CausalRefutation::estimated_effect

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    fn CausalRefutation::estimated_effect(self : CausalRefutation) -> Double

    The original estimate.

    CausalRefutation::is_significant

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    fn CausalRefutation::is_significant(self : CausalRefutation) -> Bool

    Whether the refutation is statistically significant.

    CausalRefutation::new

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    fn CausalRefutation::new(estimated_effect : Double, new_effect : Double, refutation_type : String) -> CausalRefutation

    A refutation without a significance test.

    CausalRefutation::new_effect

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    fn CausalRefutation::new_effect(self : CausalRefutation) -> Double

    The estimate after the refutation.

    CausalRefutation::new_effect_lower

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    fn CausalRefutation::new_effect_lower(self : CausalRefutation) -> Double

    The lower bound of the refuted estimates.

    CausalRefutation::new_effect_upper

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    fn CausalRefutation::new_effect_upper(self : CausalRefutation) -> Double

    The upper bound of the refuted estimates.

    CausalRefutation::p_value

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    fn CausalRefutation::p_value(self : CausalRefutation) -> Double

    The p-value of the significance test.

    CausalRefutation::refutation_type

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    fn CausalRefutation::refutation_type(self : CausalRefutation) -> String

    The name of the refutation.

    CausalRefutation::to_string

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    fn CausalRefutation::to_string(self : CausalRefutation) -> String

    A human readable summary of the refutation.

    refute_add_unobserved_common_cause

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    fn refute_add_unobserved_common_cause(estimator : 
    CausalEstimator    , estimand : 
    IdentifiedEstimand    , data : 
    DataFrame    , treatment_name : String, outcome_name : String, epsilon : Double, num_simulations : Int, treatment_value : Double, control_value : Double, target_units : String, random : 
    Random    ) -> CausalRefutation

    The unobserved common cause refuter.

    An unobserved confounder is simulated with the strength epsilon and the effect is re-estimated; the refutation reports how much the estimate moves when the confounder is strong.

    refute_bootstrap

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    fn refute_bootstrap(estimator : 
    CausalEstimator    , estimand : 
    IdentifiedEstimand    , data : 
    DataFrame    , num_simulations : Int, sample_size_fraction : Double, treatment_value : Double, control_value : Double, target_units : String, random : 
    Random    ) -> CausalRefutation

    The bootstrap refuter.

    The data is resampled with replacement and the effect is re-estimated on every resample; the refutation reports the mean of the resampled estimates and the interval they span.

    refute_data_subset

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    fn refute_data_subset(estimator : 
    CausalEstimator    , estimand : 
    IdentifiedEstimand    , data : 
    DataFrame    , subset_fraction : Double, num_simulations : Int, treatment_value : Double, control_value : Double, target_units : String, random : 
    Random    ) -> CausalRefutation

    The data subset refuter.

    The effect is re-estimated on random subsets of the data; a robust estimate does not depend on the subset.

    refute_dummy_outcome

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    fn refute_dummy_outcome(estimator : 
    CausalEstimator    , estimand : 
    IdentifiedEstimand    , data : 
    DataFrame    , outcome_name : String, num_simulations : Int, treatment_value : Double, control_value : Double, target_units : String, random : 
    Random    ) -> CausalRefutation

    The dummy outcome refuter.

    The outcome is replaced by a random variable; the refuted effect must be close to zero.

    refute_placebo_treatment

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    fn refute_placebo_treatment(estimator : 
    CausalEstimator    , estimand : 
    IdentifiedEstimand    , data : 
    DataFrame    , treatment_name : String, num_simulations : Int, treatment_value : Double, control_value : Double, target_units : String, random : 
    Random    ) -> CausalRefutation

    The placebo treatment refuter.

    The treatment is replaced by a random permutation of itself; a causal estimate that survives the refutation is not credible.

    refute_random_common_cause

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    fn refute_random_common_cause(estimator : 
    CausalEstimator    , _estimand : 
    IdentifiedEstimand    , data : 
    DataFrame    , num_simulations : Int, treatment_value : Double, control_value : Double, target_units : String, random : 
    Random    ) -> CausalRefutation

    The random common cause refuter.

    A random variable is added to the adjustment set; a robust estimate does not change.

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