riantr/moonbit_dowhy/model

README
- ::causal_graphCausalModel::causal_graph
- ::dataCausalModel::data
- ::estimate_effectCausalModel::estimate_effect
- ::identified_estimandCausalModel::identified_estimand
- ::identify_effectCausalModel::identify_effect
- ::newCausalModel::new
- ::refute_estimateCausalModel::refute_estimate

riantr/moonbit_dowhy/model does not have a README file

CausalEstimate

pub struct CausalEstimate {
  value : Double
  treatment_name : String
  outcome_name : String
  target_estimand : String
  realized_estimand_expr : String
  control_value : Double
  treatment_value : Double
  propensity_scores : Array[Double]
  ci_lower : Double
  ci_upper : Double
  standard_error : Double
  p_value : Double
  conditional_estimates : Array[(String, Double)]
  params : Map[String, String]
}

The result of a causal estimation.

This is the port of dowhy.causal_estimator.CausalEstimate.

CausalEstimate::add_conditional_estimate

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fn CausalEstimate::add_conditional_estimate(self : CausalEstimate, key : String, value : Double) -> Unit

Attach a conditional estimate.

CausalEstimate::add_param

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fn CausalEstimate::add_param(self : CausalEstimate, key : String, value : String) -> Unit

Attach a parameter to the estimate.

CausalEstimate::confidence_interval_lower

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fn CausalEstimate::confidence_interval_lower(self : CausalEstimate) -> Double

The lower bound of the confidence interval.

CausalEstimate::confidence_interval_upper

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fn CausalEstimate::confidence_interval_upper(self : CausalEstimate) -> Double

The upper bound of the confidence interval.

CausalEstimate::estimate

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fn CausalEstimate::estimate(self : CausalEstimate) -> Double

The point estimate.

CausalEstimate::new

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fn CausalEstimate::new(value : Double, treatment_name : String, outcome_name : String, target_estimand : String, control_value : Double, treatment_value : Double) -> CausalEstimate

An estimate without any interval information.

CausalEstimate::p_value

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

The p-value of the significance test.

CausalEstimate::propensity_scores

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fn CausalEstimate::propensity_scores(self : CausalEstimate) -> Array[Double]

The propensity scores the estimator computed.

CausalEstimate::set_confidence_interval

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fn CausalEstimate::set_confidence_interval(self : CausalEstimate, lower : Double, upper : Double) -> Unit

Set the confidence interval of the estimate.

CausalEstimate::set_p_value

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fn CausalEstimate::set_p_value(self : CausalEstimate, p : Double) -> Unit

Set the p-value of the estimate.

CausalEstimate::set_propensity_scores

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fn CausalEstimate::set_propensity_scores(self : CausalEstimate, scores : Array[Double]) -> Unit

Attach the propensity scores to the estimate.

CausalEstimate::set_standard_error

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fn CausalEstimate::set_standard_error(self : CausalEstimate, se : Double) -> Unit

Set the standard error of the estimate.

CausalEstimate::standard_error

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fn CausalEstimate::standard_error(self : CausalEstimate) -> Double

The standard error of the estimate.

CausalEstimate::target_estimand

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fn CausalEstimate::target_estimand(self : CausalEstimate) -> String

The estimand the estimate was computed for.

CausalEstimate::to_string

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

A human readable summary of the estimate.

Source Files

estimate.mbt

Install

Download zip

MoonBit port of DoWhy 0.14 (PyWhy). Causal graph construction and analysis, effect identification (backdoor / frontdoor / complete adjustment), estimation (linear regression, generalized linear model, propensity score weighting / matching / stratification, doubly robust, instrumental variable, two stage, regression discontinuity), refutation (bootstrap, placebo treatment, data subset, dummy outcome, unobserved common cause), intervention sampling (do samplers), temporal graphs, causal discovery (the PC algorithm with the Fisher-z test) and the portable core of the graphical causal models (the p-value merging, the bootstrap confidence intervals, the Kullback-Leibler divergences, the density estimators, the Shapley values, the independence tests, the prediction models, the causal mechanisms, the fitting of a causal model and the what-if queries (the interventional samples, the counterfactual samples and the average causal effect). Pure MoonBit with zero external runtime dependencies.

causal-inferencedowhycausal-graphcausal-discoverybackdoorfrontdoorpropensity-scoretreatment-effectrefutationidentifiersestimatorsshapley-valuesindependence-teststructural-causal-modelcausal-mechanismwhat-ifcounterfactual

riantr

MIT