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0.15.1
  • EconML User Guide
    • Overview
    • Machine Learning Based Estimation of Heterogeneous Treatment Effects
    • Motivating Examples
      • Recommendation A/B testing
      • Customer Segmentation
      • Multi-investment Attribution
    • Introduction to Causal Inference
    • Problem Setup and API Design
      • API of Conditional Average Treatment Effect Package
      • Linear in Treatment CATE Estimators
      • Example Use of API
    • Library Flow Chart
    • Detailed estimator comparison
    • Estimation Methods under Unconfoundedness
      • Orthogonal/Double Machine Learning
        • What is it?
        • What are the relevant estimator classes?
        • When should you use it?
        • Overview of Formal Methodology
        • Class Hierarchy Structure
        • Usage FAQs
        • Usage Examples
      • Doubly Robust Learning
        • What is it?
        • What are the relevant estimator classes?
        • When should you use it?
        • Overview of Formal Methodology
        • Class Hierarchy Structure
        • Usage FAQs
        • Usage Examples
      • Forest Based Estimators
        • What is it?
        • What are the relevant estimator classes?
        • When should you use it?
        • Overview of Formal Methodology
        • Class Hierarchy Structure
        • Usage Examples
      • Meta-Learners
        • What is it?
        • What are the relevant estimator classes?
        • When should you use it?
        • Overview of Formal Methodology
        • Class Hierarchy Structure
        • Usage Examples
    • Estimation Methods with Instruments
      • Deep Instrumental Variables
      • Sieve 2SLS Instrumental Variable Estimation
      • Orthogonal instrumental variables
        • What is it?
        • What are the relevant estimator classes?
        • When should you use it?
        • Class Hierarchy Structure
        • Usage Examples
    • Estimation Methods for Dynamic Treatment Regimes
      • Dynamic Double Machine Learning
        • What is it?
        • What are the relevant estimator classes?
        • When should you use it?
        • Class Hierarchy Structure
        • Usage FAQs
    • Inference
      • Bootstrap Inference
      • OLS Inference
      • Debiased Lasso Inference
      • Subsampled Honest Forest Inference
      • OrthoForest Bootstrap of Little Bags Inference
    • Model Selection
    • Interpretability
      • Tree Interpreter
      • Policy Interpreter
      • SHAP
    • Federated Learning in EconML
      • Overview
      • Motivation for Incorporating Federated Learning into the EconML Library
      • Federated Learning with EconML
        • Introducing the FederatedEstimator
        • Example Usage
      • Theory
    • References
    • Frequently Asked Questions (FAQ)
      • When should I use EconML?
      • What are the advantages of EconML?
      • How do I know if the results make sense?
      • I’m getting causal estimates that don’t make sense. What next?
      • What if I don’t have a good instrument, can’t run an experiment, and don’t observe all confounders?
      • How can I test whether I’m identifying the causal effect?
      • How do I give feedback?
    • Community
  • Public Module Reference
    • CATE Estimators
      • Double Machine Learning (DML)
        • econml.dml.DML
        • econml.dml.LinearDML
        • econml.dml.SparseLinearDML
        • econml.dml.CausalForestDML
        • econml.dml.NonParamDML
        • econml.dml.KernelDML
      • Doubly Robust (DR)
        • econml.dr.DRLearner
        • econml.dr.LinearDRLearner
        • econml.dr.SparseLinearDRLearner
        • econml.dr.ForestDRLearner
      • Meta-Learners
        • econml.metalearners.XLearner
        • econml.metalearners.TLearner
        • econml.metalearners.SLearner
        • econml.metalearners.DomainAdaptationLearner
      • Orthogonal Random Forest (ORF)
        • econml.orf.DMLOrthoForest
        • econml.orf.DROrthoForest
    • Instrumental Variable CATE Estimators
      • Double Machine Learning (DML) IV
        • econml.iv.dml.OrthoIV
        • econml.iv.dml.DMLIV
        • econml.iv.dml.NonParamDMLIV
      • Doubly Robust (DR) IV
        • econml.iv.dr.DRIV
        • econml.iv.dr.LinearDRIV
        • econml.iv.dr.SparseLinearDRIV
        • econml.iv.dr.ForestDRIV
        • econml.iv.dr.IntentToTreatDRIV
        • econml.iv.dr.LinearIntentToTreatDRIV
      • DeepIV
        • econml.iv.nnet.DeepIV
      • Sieve Methods
        • econml.iv.sieve.SieveTSLS
        • econml.iv.sieve.HermiteFeatures
        • econml.iv.sieve.DPolynomialFeatures
    • Estimators for Panel Data
      • Dynamic Double Machine Learning
        • econml.panel.dml.DynamicDML
    • Policy Learning
      • econml.policy.DRPolicyForest
      • econml.policy.DRPolicyTree
      • econml.policy.PolicyForest
      • econml.policy.PolicyTree
    • CATE Interpreters
      • econml.cate_interpreter.SingleTreeCateInterpreter
      • econml.cate_interpreter.SingleTreePolicyInterpreter
    • CATE Validation
      • econml.validate.DRTester
      • econml.validate.BLPEvaluationResults
      • econml.validate.CalibrationEvaluationResults
      • econml.validate.UpliftEvaluationResults
      • econml.validate.EvaluationResults
    • CATE Scorers
      • econml.score.RScorer
      • econml.score.EnsembleCateEstimator
    • Generalized Random Forests
      • econml.grf.CausalForest
      • econml.grf.CausalIVForest
      • econml.grf.RegressionForest
      • econml.grf.MultiOutputGRF
      • econml.grf.LinearMomentGRFCriterion
      • econml.grf.LinearMomentGRFCriterionMSE
      • econml.grf._base_grf.BaseGRF
      • econml.grf._base_grftree.GRFTree
    • Scikit-Learn Extensions
      • Linear Model Extensions
        • econml.sklearn_extensions.linear_model.DebiasedLasso
        • econml.sklearn_extensions.linear_model.MultiOutputDebiasedLasso
        • econml.sklearn_extensions.linear_model.SelectiveRegularization
        • econml.sklearn_extensions.linear_model.StatsModelsLinearRegression
        • econml.sklearn_extensions.linear_model.StatsModelsRLM
        • econml.sklearn_extensions.linear_model.WeightedLasso
        • econml.sklearn_extensions.linear_model.WeightedLassoCV
        • econml.sklearn_extensions.linear_model.WeightedMultiTaskLassoCV
        • econml.sklearn_extensions.linear_model.WeightedLassoCVWrapper
      • Model Selection Extensions
        • econml.sklearn_extensions.model_selection.GridSearchCVList
        • econml.sklearn_extensions.model_selection.WeightedKFold
        • econml.sklearn_extensions.model_selection.WeightedStratifiedKFold
    • Inference
      • Inference Results
        • econml.inference.NormalInferenceResults
        • econml.inference.EmpiricalInferenceResults
        • econml.inference.PopulationSummaryResults
      • Inference Methods
        • econml.inference.BootstrapInference
        • econml.inference.GenericModelFinalInference
        • econml.inference.GenericSingleTreatmentModelFinalInference
        • econml.inference.LinearModelFinalInference
        • econml.inference.StatsModelsInference
        • econml.inference.GenericModelFinalInferenceDiscrete
        • econml.inference.LinearModelFinalInferenceDiscrete
        • econml.inference.StatsModelsInferenceDiscrete
    • Federated Estimation
      • econml.federated_learning.FederatedEstimator
    • Solutions
      • Causal Analysis
        • econml.solutions.causal_analysis.CausalAnalysis
    • Integration with DoWhy
      • econml.dowhy.DoWhyWrapper
    • Utilities
      • econml.utilities
  • Private Module Reference
    • econml._ortho_learner
    • econml._cate_estimator
    • econml.dml._rlearner
    • econml.inference._bootstrap
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