py-why/EconML

Features

• Causal Inference Tools - Provides a comprehensive suite of tools for identifying and measuring cause-effect relationships in observational data.
• Heterogeneous Treatment Effect Estimators - Calculates the causal effect of a treatment for individual units using algorithms optimized for unbalanced datasets.
• Causal Machine Learning Integrations - Uses machine learning to remove bias from treatment estimates by isolating the causal signal from high-dimensional confounders.
• Causal Forests - Implements causal forests to calculate treatment effects using double machine learning and random forest similarity metrics.
• Doubly Robust Estimators - Ships doubly robust estimators that merge propensity and outcome models to ensure valid causal estimates.
• Instrumental Variable Estimators - Calculates heterogeneous treatment effects using a doubly robust instrumental variable approach.
• Causal Estimate Refuters - Ships causal estimate refuters to validate robustness by introducing random confounders or placebo variables.
• Causal Policy Learning - Predicts the optimal recommended treatment for users using doubly robust policy learning methods.
• Double Machine Learning - Removes bias from treatment effect estimates by using machine learning models to isolate the causal signal.
• Average Treatment Effect Calculation - Calculates the average treatment effect as the mean difference in outcomes between treatment points across a population.
• Heterogeneity Driver Identification - Trains shallow decision trees to isolate the feature cutoffs that maximize differences in treatment response.
• Orthogonalization Methods - Implements orthogonal machine learning to isolate causal signals by removing the influence of confounding variables.
• Treatment Effect Predictors - Calculates point estimates, confidence intervals, and covariance matrices for local parameters at specific data points.
• Instrumental Variable Orthogonalization - Provides instrumental variable orthogonalization to recover causal effects when unobserved confounding is present.
• Causal Forests - Implements causal forests to estimate non-parametric treatment effects using honest random forest architectures.
• Causal Model Interpretability - Analyzes complex treatment effect models using tree-based interpreters to uncover the characteristics driving outcomes.
• Causal Assumption Validators - Provides causal assumption validators to assess how unobserved confounding affects the reliability of estimates.
• Causal Inference Frameworks - Provides a structured framework for modeling the causal impact of sequential treatments over time.
• Causal Effect Estimators - Provides interfaces to estimate and then interpret the magnitudes of causal effects from observational data.
• Doubly Robust Forests - Estimates effects for categorical treatments by solving local regressions based on doubly robust moments and forests.
• Imperfect Compliance Estimators - Uses instrumental variables to isolate random variation in treatment assignment for non-compliant experiment subjects.
• Marginal Effect Calculators - Computes the derivative of the treatment effect around a specific point to determine the immediate impact.
• Population Effect Summarizers - Provides capabilities to calculate mean point estimates, standard errors, and confidence intervals for population treatment effects.
• Cross-Fitting Sample Splitting - Implements cross-fitting sample splitting to ensure nuisance models do not overfit, providing valid causal estimates.
• Treatment Policy Learning - Provides offline doubly robust policy learning to predict optimal recommended treatments for given features.
• Confidence Interval Calculators - Calculates statistical ranges to determine the significance of predicted causal effects.
• Individual Prediction Intervals - Calculates standard errors and confidence intervals for individual treatment effect predictions.
• Treatment Effect Uncertainty Quantification - Computes uncertainty intervals for treatment effects using bootstrapping, OLS, or debiased lasso methods.
• Dynamic Treatment Regime Analysis - Evaluates the causal impact of sequences of treatments over time using dynamic double machine learning.
• Feature Contribution Analysis - Quantifies how individual features contribute to the heterogeneity of estimated causal parameters.
• Federated Causal Learners - Enables treatment effect estimation across distributed datasets without sharing raw data between parties.
• Federated Treatment Aggregators - Combines multiple pre-fit causal estimators to estimate heterogeneous treatment effects across distributed data sources.
• Intent-to-Treat Estimators - Calculates heterogeneous treatment effects for A/B test settings using the DRIV algorithm to handle observational data.
• Local Parameter Estimators - Implements local parameter estimation by solving linear moment equations to identify treatment effects in specific subpopulations.
• Marginal Treatment Effect Analysis - Determines the marginal impact of a treatment on an outcome for both constant and heterogeneous effects.
• Multi-Treatment Attribution - Estimates the causal impact of several discrete outreach efforts while filtering out confounding correlations.
• Personalized Treatment Policies - Determines the optimal intervention for specific individuals by predicting counterfactual outcomes to maximize a target metric.
• Treatment Effect Visualizations - Display the accuracy of estimated treatment effects using calibration plots, QINI curves, and TOC curves.
• Treatment Policy Construction - Allows for the construction of personalized treatment policies by segmenting samples based on costs and effect signs.
• Treatment Policy Optimization - Determines the best treatment assignments for individual units using doubly-robust corrections to maximize counterfactual outcomes.
• Uplift Models - Validates treatment effect models using uplift curves and specific validation metrics.
• Feature Expansion - Provides methods for generating polynomial or interaction features to capture non-linear heterogeneity in treatment effects.
• Debiased Lasso - Provides debiased lasso regression to estimate linear coefficients with valid standard errors in high-dimensional settings.
• Local Moment Trees - Solves local moment equation problems by partitioning data into regions where parameter estimates are calculated.
• Model Performance Evaluators - Provides model performance evaluators to calculate R-square analogues for treatment effect models.
• Nuisance Model Selection - Provides tools to optimize the choice of nuisance models to improve the accuracy of treatment effect estimation.
• Honest Forests - Implements honest random forests to prevent overfitting in causal effect estimation.
• Honest Random Forests - Fits subsampled honest random forest regressors using separate data samples for tree structure and leaf estimation.
• Sequential Treatment Estimation - Estimates heterogeneous treatment effects for interventions offered sequentially via adaptive policies.
• Causal Counterfactual Simulators - Implements modules to compute alternative outcomes by intervening on causal models and simulating hypothetical scenarios.
• CATE Model Validation - Provides tools to evaluate the accuracy of conditional average treatment effect models through calibration metrics.
• Temporal Causal Analysis - Estimates how treatment effects evolve over time within panel data settings with repeated observations.
• Uplift Modeling - Implements uplift modeling to measure treatment effectiveness using QINI coefficients and uplift curves.
• Debiased Lasso Estimators - Computes linear coefficients with L1 regularization and applies a debiasing correction to provide standard errors.
• Orthogonal Forests - Estimates non-parametric treatment effects while controlling for high-dimensional confounders using random forests and cross-fitting.
• Bootstrap Confidence Estimation - Computes confidence intervals for causal estimators using repeated resampling of observational data.
• Prediction Calibrations - Implements prediction calibrations to compare predicted group average treatment effects against observed values.
• Causal Graph Creators - Integrates causal graphs with estimators and refutation tests to validate causal inference results.
• Causal Inference - Heterogeneous Treatment Effects Estimation by Microsoft.

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