riantr/moonbit_dowhy/estimators

README
- ::causal_graphCausalModel::causal_graph
- ::dataCausalModel::data
- ::estimand_typeCausalModel::estimand_type
- ::estimate_effectCausalModel::estimate_effect
- ::get_common_causesCausalModel::get_common_causes
- ::get_effect_modifiersCausalModel::get_effect_modifiers
- ::get_estimatorCausalModel::get_estimator
- ::get_instrumentsCausalModel::get_instruments
- ::graph_with_rolesCausalModel::graph_with_roles
- ::identified_estimandCausalModel::identified_estimand
- ::identifies_variablesCausalModel::identifies_variables
- ::identify_effectCausalModel::identify_effect
- ::newCausalModel::new
- ::new_with_graph_stringCausalModel::new_with_graph_string
- ::proceeds_when_unidentifiableCausalModel::proceeds_when_unidentifiable
- ::refute_assess_overlapCausalModel::refute_assess_overlap
- ::refute_estimateCausalModel::refute_estimate
- ::summaryCausalModel::summary
- ::unobserved_graph_variablesCausalModel::unobserved_graph_variables
- ::unused_data_variablesCausalModel::unused_data_variables
V
is_supported_graph_format

riantr/moonbit_dowhy/estimators does not have a README file

CausalEstimator

pub struct CausalEstimator {
  target_estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand
  kind : EstimatorKind
  weighting_scheme : WeightingScheme
  min_ps_score : Double
  max_ps_score : Double
  num_strata : NumStrata
  clipping_threshold : Int
  instrument_name : String
  rd_variable_name : String
  rd_threshold_value : Double
  rd_bandwidth : Double
  rd_local_data : @riantr/moonbit_dowhy/api.DataFrame
DataFrame?
  model : @riantr/moonbit_dowhy/utils.OlsFit
OlsFit
  first_stage : TwoStageModel?
  second_stage : TwoStageModel?
  glm_model : @riantr/moonbit_dowhy/utils.GlmFit
GlmFit
  glm_family : @riantr/moonbit_dowhy/utils.GlmFamily
GlmFamily
  encoders : @riantr/moonbit_dowhy/api.Encoders
Encoders
  features : Array[String]
  propensities : Array[Double]
  propensity_beta : Array[Double]
  adjustment_names : Array[String]
  effect_modifier_names : Array[String]
  treatment_name : String
  outcome_name : String
  treatment_value : Double
  control_value : Double
  confidence_level : Double
  num_simulations : Int
  sample_size_fraction : Double
  compute_confidence_intervals : Bool
  test_significance : Bool
}

A causal estimator.

The estimator owns the identified estimand and the state that fit produced; estimate_effect turns a data frame into a CausalEstimate.

CausalEstimator::adjustment_set

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fn CausalEstimator::adjustment_set(self : CausalEstimator) -> Array[String]

The adjustment variables the estimator uses.

CausalEstimator::doubly_robust

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fn CausalEstimator::doubly_robust(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand, min_ps_score : Double, max_ps_score : Double) -> CausalEstimator

A doubly robust estimator.

CausalEstimator::effect_modifiers

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fn CausalEstimator::effect_modifiers(self : CausalEstimator) -> Array[String]

The effect modifiers the estimator uses.

The bootstrap refuter perturbs them, the way the upstream does.

CausalEstimator::estimand

source

fn CausalEstimator::estimand(self : CausalEstimator) -> @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand

The estimand the estimator was built for.

CausalEstimator::estimate_conditional_effects

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fn CausalEstimator::estimate_conditional_effects(self : CausalEstimator, data : @riantr/moonbit_dowhy/api.DataFrame
DataFrame, treatment_value : Double, control_value : Double) -> Array[(String, Double)]

The conditional estimates of the effect, one per group of the effect modifier.

The key of a group is the value of the discretised effect modifier, or the values of the discretised effect modifiers when there is more than one. The list is empty when the estimator has no effect modifier, which is what the upstream reports as a None.

CausalEstimator::estimate_effect

source

fn CausalEstimator::estimate_effect(self : CausalEstimator, data : @riantr/moonbit_dowhy/api.DataFrame
DataFrame, treatment_value : Double, control_value : Double, target_units : String) -> @riantr/moonbit_dowhy/model.CausalEstimate
CausalEstimate

Estimate the treatment effect.

target_units is one of "ate", "att" or "atc".

CausalEstimator::estimator_kind

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fn CausalEstimator::estimator_kind(self : CausalEstimator) -> EstimatorKind

The algorithm of the estimator.

CausalEstimator::fit

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fn CausalEstimator::fit(self : CausalEstimator, data : @riantr/moonbit_dowhy/api.DataFrame
DataFrame, effect_modifier_names : Array[String]) -> Unit

Fit the estimator on the given data.

The estimator remembers the adjustment variables of its estimand, fits the model it needs and stores the propensity scores when it uses them.

CausalEstimator::generalized_linear_model

source

fn CausalEstimator::generalized_linear_model(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand, family : @riantr/moonbit_dowhy/utils.GlmFamily
GlmFamily) -> CausalEstimator

A generalised linear model estimator for the given estimand.

The family decides the link of the model: a binomial family fits a logistic regression, a poisson family fits a count model and the gaussian family fits an ordinary least squares model.

CausalEstimator::instrument_name

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fn CausalEstimator::instrument_name(self : CausalEstimator) -> String

The name of the instrument of an instrumental variable estimator.

It is empty for every other estimator.

CausalEstimator::instrumental_variable

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fn CausalEstimator::instrumental_variable(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand, instrument_name : String) -> CausalEstimator

An instrumental variable estimator.

CausalEstimator::intervene

source

fn CausalEstimator::intervene(self : CausalEstimator, value : Double, data : @riantr/moonbit_dowhy/api.DataFrame
DataFrame) -> Double

The mean outcome of the intervention do(treatment = value).

This is the port of the do operator of the upstream estimators: the treatment is set to the given value in every row of the data and the model predicts the outcome of the modified data.

CausalEstimator::linear_regression

source

fn CausalEstimator::linear_regression(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand) -> CausalEstimator

An ordinary least squares estimator for the given estimand.

CausalEstimator::outcome

source

fn CausalEstimator::outcome(self : CausalEstimator) -> String

The outcome column the estimator uses.

CausalEstimator::propensity_score_matching

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fn CausalEstimator::propensity_score_matching(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand) -> CausalEstimator

A propensity score matching estimator.

CausalEstimator::propensity_score_stratification

source

fn CausalEstimator::propensity_score_stratification(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand, num_strata : NumStrata, clipping_threshold : Int) -> CausalEstimator

A propensity score stratification estimator.

CausalEstimator::propensity_score_weighting

source

fn CausalEstimator::propensity_score_weighting(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand, min_ps_score : Double, max_ps_score : Double, scheme : WeightingScheme) -> CausalEstimator

An inverse propensity score weighting estimator.

CausalEstimator::propensity_scores

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

The propensity scores the estimator computed.

CausalEstimator::regression_discontinuity

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fn CausalEstimator::regression_discontinuity(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand, rd_variable_name : String, rd_threshold_value : Double, rd_bandwidth : Double) -> CausalEstimator

A regression discontinuity estimator.

CausalEstimator::set_compute_confidence_intervals

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fn CausalEstimator::set_compute_confidence_intervals(self : CausalEstimator, compute : Bool) -> Unit

Ask the estimator to compute the confidence intervals.

CausalEstimator::set_confidence_level

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fn CausalEstimator::set_confidence_level(self : CausalEstimator, level : Double) -> Unit

Set the confidence level of the intervals.

CausalEstimator::set_estimand

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fn CausalEstimator::set_estimand(self : CausalEstimator, estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand) -> Unit

Replace the estimand the estimator was built for.

The refuters use this to estimate a different effect with the same estimator.

CausalEstimator::set_num_simulations

source

fn CausalEstimator::set_num_simulations(self : CausalEstimator, num_simulations : Int) -> Unit

Set the number of bootstrap simulations.

CausalEstimator::set_sample_size_fraction

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fn CausalEstimator::set_sample_size_fraction(self : CausalEstimator, fraction : Double) -> Unit

Set the fraction of the sample a bootstrap resample takes.

CausalEstimator::set_test_significance

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fn CausalEstimator::set_test_significance(self : CausalEstimator, do_test : Bool) -> Unit

Ask the estimator to test the significance of the estimate.

CausalEstimator::treatment

source

fn CausalEstimator::treatment(self : CausalEstimator) -> String

The treatment column the estimator uses.

CausalEstimator::two_stage_error

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fn CausalEstimator::two_stage_error(self : CausalEstimator) -> String

The error the two stage regression estimator reports for the estimand of the estimator, or an empty string when it can estimate the effect.

The upstream raises a ValueError when the estimand holds no mediator variable or more than one, and it raises an Exception when the estimand holds more than one treatment variable or more than one outcome variable. MoonBit cannot catch an error, so the port aborts with the message of the upstream; this function reports the message of the error the upstream raises, so that a caller can check for it before it fits the estimator.

CausalEstimator::two_stage_features

source

fn CausalEstimator::two_stage_features(self : CausalEstimator) -> (Array[String], Array[String])

The columns of the design matrix of the two stages of the two stage regression estimator.

The first element holds the columns of the first stage, the one that regresses the mediator on the treatment, and the second one the columns of the second stage, the one that regresses the outcome on the mediator.

CausalEstimator::two_stage_regression

source

fn CausalEstimator::two_stage_regression(estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand) -> CausalEstimator

A two stage regression estimator.

CausalEstimator::with_estimand

source

fn CausalEstimator::with_estimand(self : CausalEstimator, estimand : @riantr/moonbit_dowhy/identifiers.IdentifiedEstimand
IdentifiedEstimand) -> CausalEstimator

A copy of the estimator that estimates a different estimand.

The refuters use this to re-estimate the effect with the same algorithm on modified data. The copy starts without any fitted state, so the estimator that was handed in keeps the model and the adjustment set it was fitted with.

CausalEstimator::with_instrument_name

source

fn CausalEstimator::with_instrument_name(self : CausalEstimator, name : String) -> CausalEstimator

A copy of the estimator that uses a different instrument.

The placebo treatment refuter uses this for the instrumental variable method, whose instrument the placebo replaces.

EstimatorKind

source

pub enum EstimatorKind {
  LinearRegression
  PropensityScoreWeighting
  PropensityScoreMatching
  PropensityScoreStratification
  DoublyRobust
  InstrumentalVariable
  TwoStageRegression
  RegressionDiscontinuity
  GeneralizedLinearModel
}

The estimator family that DoWhy offers.

The kind decides which algorithm fit and estimate_effect run; the estimator itself keeps the fitted state, exactly like the Python classes inherit from CausalEstimator.

NumStrata

source

pub enum NumStrata {
  Fixed(Int)
  Auto
}

The number of strata of the propensity score stratification estimator.

The upstream takes the string "auto", which searches for the number of strata, and an integer, which is the number of strata.

NumStrata::auto

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fn NumStrata::auto() -> NumStrata

The number of strata is searched for, the way the upstream searches for it.

NumStrata::fixed

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fn NumStrata::fixed(count : Int) -> NumStrata

The given number of strata.

TwoStageModel

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type TwoStageModel

One stage of the two stage regression estimator.

Every stage has its own encoders, because the treatment of the first stage is the column the second stage regresses on.

WeightingScheme

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pub enum WeightingScheme {
  IpsWeight
  IpsStabilizedWeight
  IpsNormalizedWeight
}

The weighting scheme of the propensity score weighting estimator.

WeightingScheme::ips_normalized_weight

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fn WeightingScheme::ips_normalized_weight() -> WeightingScheme

The normalised inverse propensity score weighting scheme.

WeightingScheme::ips_stabilized_weight

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fn WeightingScheme::ips_stabilized_weight() -> WeightingScheme

The stabilised inverse propensity score weighting scheme.

WeightingScheme::ips_weight

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fn WeightingScheme::ips_weight() -> WeightingScheme

The inverse propensity score weighting scheme.

WeightingScheme::name

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fn WeightingScheme::name(self : WeightingScheme) -> String

The name of the weighting scheme.

DEFAULT_CONFIDENCE_LEVEL

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let DEFAULT_CONFIDENCE_LEVEL : Double

The default confidence level.

DEFAULT_NUMBER_OF_SIMULATIONS_CI

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let DEFAULT_NUMBER_OF_SIMULATIONS_CI : Int

The default number of simulations used for the confidence intervals.

DEFAULT_NUMBER_OF_SIMULATIONS_STAT_TEST

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let DEFAULT_NUMBER_OF_SIMULATIONS_STAT_TEST : Int

The default number of simulations used for the significance test.

NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS

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let NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS : Int

The default number of quantiles a continuous effect modifier is split into.

Source Files

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, the assess overlap refuter that learns the rules of the support and of the overlap with boolean rules), 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, the what-if queries (the interventional samples, the counterfactual samples and the average causal effect), the validation layer (the kernel based conditional independence test, the validation of a causal structure and the validation of an invertible model) and the falsification layer (the local Markov condition, the pairwise dependencies, the parental d-separations, the causal minimality and the permutation test of a graph) and the model evaluation layer (the normalised mean squared error, the continuous ranked probability score, the performance of the causal mechanisms, the test of the invertibility assumption and the report of the evaluation) and the distribution change layer (the test of the change of a mechanism, the attribution of the change of the distribution of a target node, the multiply-robust attribution of the change of the mean or of the variance of a target node and the scores of the change of the distribution of every node). Pure MoonBit with zero external runtime dependencies.

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

riantr

MIT

gitee.com/ren-yongxiang/moonbit_doWhy

Updated 10 hours ago

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#CausalEstimator

#CausalEstimator::adjustment_set

#CausalEstimator::doubly_robust

#CausalEstimator::effect_modifiers

#CausalEstimator::estimand

#CausalEstimator::estimate_conditional_effects

#CausalEstimator::estimate_effect

#CausalEstimator::estimator_kind

#CausalEstimator::fit

#CausalEstimator::generalized_linear_model

#CausalEstimator::instrument_name

#CausalEstimator::instrumental_variable

#CausalEstimator::intervene

#CausalEstimator::linear_regression

#CausalEstimator::outcome

#CausalEstimator::propensity_score_matching

#CausalEstimator::propensity_score_stratification

#CausalEstimator::propensity_score_weighting

#CausalEstimator::propensity_scores

#CausalEstimator::regression_discontinuity

#CausalEstimator::set_compute_confidence_intervals

#CausalEstimator::set_confidence_level

#CausalEstimator::set_estimand

#CausalEstimator::set_num_simulations

#CausalEstimator::set_sample_size_fraction

#CausalEstimator::set_test_significance

#CausalEstimator::treatment

#CausalEstimator::two_stage_error

#CausalEstimator::two_stage_features

#CausalEstimator::two_stage_regression

#CausalEstimator::with_estimand

#CausalEstimator::with_instrument_name

#EstimatorKind

#NumStrata

#NumStrata::auto

#NumStrata::fixed

#TwoStageModel

#WeightingScheme

#WeightingScheme::ips_normalized_weight

#WeightingScheme::ips_stabilized_weight

#WeightingScheme::ips_weight

#WeightingScheme::name

#DEFAULT_CONFIDENCE_LEVEL

#DEFAULT_NUMBER_OF_SIMULATIONS_CI

#DEFAULT_NUMBER_OF_SIMULATIONS_STAT_TEST

#NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS

Source Files

CausalEstimator

CausalEstimator::adjustment_set

CausalEstimator::doubly_robust

CausalEstimator::effect_modifiers

CausalEstimator::estimand

CausalEstimator::estimate_conditional_effects

CausalEstimator::estimate_effect

CausalEstimator::estimator_kind

CausalEstimator::fit

CausalEstimator::generalized_linear_model

CausalEstimator::instrument_name

CausalEstimator::instrumental_variable

CausalEstimator::intervene

CausalEstimator::linear_regression

CausalEstimator::outcome

CausalEstimator::propensity_score_matching

CausalEstimator::propensity_score_stratification

CausalEstimator::propensity_score_weighting

CausalEstimator::propensity_scores

CausalEstimator::regression_discontinuity

CausalEstimator::set_compute_confidence_intervals

CausalEstimator::set_confidence_level

CausalEstimator::set_estimand

CausalEstimator::set_num_simulations

CausalEstimator::set_sample_size_fraction

CausalEstimator::set_test_significance

CausalEstimator::treatment

CausalEstimator::two_stage_error

CausalEstimator::two_stage_features

CausalEstimator::two_stage_regression

CausalEstimator::with_estimand

CausalEstimator::with_instrument_name

EstimatorKind

NumStrata

NumStrata::auto

NumStrata::fixed

TwoStageModel

WeightingScheme

WeightingScheme::ips_normalized_weight

WeightingScheme::ips_stabilized_weight

WeightingScheme::ips_weight

WeightingScheme::name

DEFAULT_CONFIDENCE_LEVEL

DEFAULT_NUMBER_OF_SIMULATIONS_CI

DEFAULT_NUMBER_OF_SIMULATIONS_STAT_TEST

NUM_QUANTILES_TO_DISCRETIZE_CONT_COLS