Public Module Reference

CATE Estimators

Double Machine Learning (DML)

`econml.dml.DML`(*, model_y, model_t, model_final)	The base class for parametric Double ML estimators.
`econml.dml.LinearDML`(*[, model_y, model_t, ...])	The Double ML Estimator with a low-dimensional linear final stage implemented as a statsmodel regression.
`econml.dml.SparseLinearDML`(*[, model_y, ...])	A specialized version of the Double ML estimator for the sparse linear case.
`econml.dml.CausalForestDML`(*[, model_y, ...])	A Causal Forest [cfdml1] combined with double machine learning based residualization of the treatment and outcome variables.
`econml.dml.NonParamDML`(*, model_y, model_t, ...)	The base class for non-parametric Double ML estimators, that can have arbitrary final ML models of the CATE.
`econml.dml.KernelDML`([model_y, model_t, ...])	A specialized version of the linear Double ML Estimator that uses random fourier features.

Doubly Robust (DR)

`econml.dr.DRLearner`(*[, model_propensity, ...])	CATE estimator that uses doubly-robust correction techniques to account for covariate shift (selection bias) between the treatment arms.
`econml.dr.LinearDRLearner`(*[, ...])	Special case of the `DRLearner` where the final stage is a Linear Regression on a low dimensional set of features.
`econml.dr.SparseLinearDRLearner`(*[, ...])	Special case of the `DRLearner` where the final stage is a Debiased Lasso Regression.
`econml.dr.ForestDRLearner`(*[, ...])	Instance of DRLearner with a `RegressionForest` as a final model, so as to enable non-parametric inference.

Meta-Learners

`econml.metalearners.XLearner`(*, models[, ...])	Meta-algorithm proposed by Kunzel et al. that performs best in settings
`econml.metalearners.TLearner`(*, models[, ...])	Conditional mean regression estimator.
`econml.metalearners.SLearner`(*, overall_model)	Conditional mean regression estimator where the treatment assignment is taken as a feature in the ML model.
`econml.metalearners.DomainAdaptationLearner`(*, ...)	Meta-algorithm that uses domain adaptation techniques to account for

Orthogonal Random Forest (ORF)

`econml.orf.DMLOrthoForest`(*[, n_trees, ...])	OrthoForest for continuous or discrete treatments using the DML residual on residual moment function.
`econml.orf.DROrthoForest`(*[, n_trees, ...])	OrthoForest for discrete treatments using the doubly robust moment function.

Instrumental Variable CATE Estimators

Double Machine Learning (DML) IV

`econml.iv.dml.OrthoIV`(*[, model_y_xw, ...])	Implementation of the orthogonal/double ml method for CATE estimation with IV as described in section 4.2:
`econml.iv.dml.DMLIV`(*[, model_y_xw, ...])	The base class for parametric DMLIV estimators to estimate a CATE.
`econml.iv.dml.NonParamDMLIV`(*[, model_y_xw, ...])	The base class for non-parametric DMLIV that allows for an arbitrary square loss based ML method in the final stage of the DMLIV algorithm.

Doubly Robust (DR) IV

`econml.iv.dr.DRIV`(*[, model_y_xw, ...])	The DRIV algorithm for estimating CATE with IVs.
`econml.iv.dr.LinearDRIV`(*[, model_y_xw, ...])	Special case of the `DRIV` where the final stage is a Linear Regression.
`econml.iv.dr.SparseLinearDRIV`(*[, ...])	Special case of the `DRIV` where the final stage is a Debiased Lasso Regression.
`econml.iv.dr.ForestDRIV`(*[, model_y_xw, ...])	Instance of DRIV with a `RegressionForest` as a final model, so as to enable non-parametric inference.
`econml.iv.dr.IntentToTreatDRIV`(*[, ...])	Implements the DRIV algorithm for the intent-to-treat A/B test setting
`econml.iv.dr.LinearIntentToTreatDRIV`(*[, ...])	Implements the DRIV algorithm for the Linear Intent-to-Treat A/B test setting

DeepIV

econml.iv.nnet.DeepIV(*, n_components, m, h, ...)

The Deep IV Estimator (see http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf).

Sieve Methods

`econml.iv.sieve.SieveTSLS`(*, t_featurizer, ...)	Non-parametric instrumental variables estimator.
`econml.iv.sieve.HermiteFeatures`(degree[, ...])	Featurizer that returns(unscaled) Hermite function evaluations.
`econml.iv.sieve.DPolynomialFeatures`([...])	Featurizer that returns the derivatives of `PolynomialFeatures` features in a way that's compatible with the expectations of `SieveTSLS`'s dt_featurizer parameter.

Estimators for Panel Data

Dynamic Double Machine Learning

econml.panel.dml.DynamicDML(*[, model_y, ...])

CATE estimator for dynamic treatment effect estimation.

Policy Learning

`econml.policy.DRPolicyForest`(*[, ...])	Policy learner that uses doubly-robust correction techniques to account for covariate shift (selection bias) between the treatment arms.
`econml.policy.DRPolicyTree`(*[, ...])	Policy learner that uses doubly-robust correction techniques to account for covariate shift (selection bias) between the treatment arms.
`econml.policy.PolicyForest`([n_estimators, ...])	Welfare maximization policy forest.
`econml.policy.PolicyTree`(*[, criterion, ...])	Welfare maximization policy tree.

CATE Interpreters

`econml.cate_interpreter.SingleTreeCateInterpreter`(*)	An interpreter for the effect estimated by a CATE estimator
`econml.cate_interpreter.SingleTreePolicyInterpreter`(*)	An interpreter for a policy estimated based on a CATE estimation

CATE Validation

`econml.validate.DRTester`(*, ...[, cv])	Validation tests for CATE models.
`econml.validate.BLPEvaluationResults`(params, ...)	Results class for BLP test.
`econml.validate.CalibrationEvaluationResults`(...)	Results class for calibration test.
`econml.validate.UpliftEvaluationResults`(...)	Results class for uplift curve-based tests.
`econml.validate.EvaluationResults`(cal_res, ...)	Results class for combination of all tests.

CATE Scorers

`econml.score.RScorer`(*, model_y, model_t[, ...])	Scorer based on the RLearner loss. Fits residual models at fit time and calculates residuals of the evaluation data in a cross-fitting manner::.
`econml.score.EnsembleCateEstimator`(*, ...)	A CATE estimator that represents a weighted ensemble of many CATE estimators.

Generalized Random Forests

`econml.grf.CausalForest`([n_estimators, ...])	A Causal Forest [cf1].
`econml.grf.CausalIVForest`([n_estimators, ...])	A Causal IV Forest [cfiv1].
`econml.grf.RegressionForest`([n_estimators, ...])	An implementation of a subsampled honest random forest regressor on top of an sklearn regression tree.
`econml.grf.MultiOutputGRF`(estimator)	Simple wrapper estimator that enables multiple outcome labels for all the grf estimators that only accept a single outcome.
`econml.grf.LinearMomentGRFCriterion`	A criterion class that estimates local parameters defined via linear moment equations of the form.
`econml.grf.LinearMomentGRFCriterionMSE`
`econml.grf._base_grf.BaseGRF`([n_estimators, ...])	Base class for Genearlized Random Forests for solving linear moment equations of the form.
`econml.grf._base_grftree.GRFTree`(*[, ...])	A tree of a Generalized Random Forest [grftree1]. This method should be used primarily through the BaseGRF forest class and its derivatives and not as a standalone estimator. It fits a tree that solves the local moment equation problem::.

Scikit-Learn Extensions

Linear Model Extensions

`econml.sklearn_extensions.linear_model.DebiasedLasso`([...])	Debiased Lasso model.
`econml.sklearn_extensions.linear_model.MultiOutputDebiasedLasso`([...])	Debiased MultiOutputLasso model.
`econml.sklearn_extensions.linear_model.SelectiveRegularization`(...)	Estimator of a linear model where regularization is applied to only a subset of the coefficients.
`econml.sklearn_extensions.linear_model.StatsModelsLinearRegression`([...])	Class which mimics weighted linear regression from the statsmodels package.
`econml.sklearn_extensions.linear_model.StatsModelsRLM`([...])	Class which mimics robust linear regression from the statsmodels package.
`econml.sklearn_extensions.linear_model.WeightedLasso`([...])	Version of sklearn Lasso that accepts weights.
`econml.sklearn_extensions.linear_model.WeightedLassoCV`([...])	Version of sklearn LassoCV that accepts weights.
`econml.sklearn_extensions.linear_model.WeightedMultiTaskLassoCV`([...])	Version of sklearn MultiTaskLassoCV that accepts weights.
`econml.sklearn_extensions.linear_model.WeightedLassoCVWrapper`(...)	Helper class to wrap either WeightedLassoCV or WeightedMultiTaskLassoCV depending on the shape of the target.

Model Selection Extensions

`econml.sklearn_extensions.model_selection.GridSearchCVList`(...)	An extension of GridSearchCV that allows for passing a list of estimators each with their own parameter grid and returns the best among all estimators in the list and hyperparameter in their corresponding grid.
`econml.sklearn_extensions.model_selection.WeightedKFold`([...])	K-Folds cross-validator for weighted data.
`econml.sklearn_extensions.model_selection.WeightedStratifiedKFold`([...])	Stratified K-Folds cross-validator for weighted data.

Inference

Inference Results

`econml.inference.NormalInferenceResults`(d_t, ...)	Results class for inference assuming a normal distribution.
`econml.inference.EmpiricalInferenceResults`(...)	Results class for inference with an empirical set of samples.
`econml.inference.PopulationSummaryResults`(...)	Population summary results class for inferences.

Inference Methods

`econml.inference.BootstrapInference`([...])	Inference instance to perform bootstrapping.
`econml.inference.GenericModelFinalInference`()	Inference based on predict_interval of the model_final model.
`econml.inference.GenericSingleTreatmentModelFinalInference`()	Inference based on predict_interval of the model_final model.
`econml.inference.LinearModelFinalInference`()	Inference based on predict_interval of the model_final model.
`econml.inference.StatsModelsInference`([cov_type])	Stores statsmodels covariance options.
`econml.inference.GenericModelFinalInferenceDiscrete`()	Assumes estimator is fitted on categorical treatment and a separate generic model_final is used to fit the CATE associated with each treatment.
`econml.inference.LinearModelFinalInferenceDiscrete`()	Inference method for estimators with categorical treatments, where a linear in X model is used for the CATE associated with each treatment.
`econml.inference.StatsModelsInferenceDiscrete`([...])	Special case where final model is a StatsModelsLinearRegression

Federated Estimation

econml.federated_learning.FederatedEstimator(...)

A class for federated learning using LinearDML, LinearDRIV, and LinearDRLearner estimators.

Solutions

Causal Analysis

econml.solutions.causal_analysis.CausalAnalysis(...)

Note: this class is experimental and the API may evolve over our next few releases.

Integration with DoWhy

econml.dowhy.DoWhyWrapper(cate_estimator)

A wrapper class to allow user call other methods from dowhy package through EconML.

Utilities

econml.utilities

Utility methods.

Private Module Reference

`econml._ortho_learner`	Orthogonal Machine Learning is a general approach to estimating causal models by formulating them as minimizers of some loss function that depends on auxiliary regression models that also need to be estimated from data.
`econml._cate_estimator`	Base classes for all CATE estimators.
`econml.dml._rlearner`	The R Learner is an approach for estimating flexible non-parametric models of conditional average treatment effects in the setting with no unobserved confounders.
`econml.inference._bootstrap`	Bootstrap sampling.