Generalized random forests

DOI10.1214/18-AOS1709zbMath1418.62102arXiv1610.01271OpenAlexW2962727190MaRDI QIDQ666599

Stefan Wager, Susan Athey, Julie Tibshirani

Publication date: 6 March 2019

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.01271

zbMATH Keywords

instrumental variable asymptotic theory causal inference generalized random forest

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks, Semiparametric estimation for average causal effects using propensity score-based spline, Recent advances in statistical methodologies in evaluating program for high-dimensional data, Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications, Linear Aggregation in Tree-Based Estimators, Dimension Reduction Forests: Local Variable Importance Using Structured Random Forests, Sufficient dimension reduction for average causal effect estimation, A Pliable Lasso, Local Linear Forests, Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon Health Insurance Experiment, Robust Inference of Conditional Average Treatment Effects Using Dimension Reduction, Contrast trees and distribution boosting, Ordinal trees and random forests: score-free recursive partitioning and improved ensembles, Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach, Random Forest Adjustment for Approximate Bayesian Computation, Consistency of survival tree and forest models: splitting bias and correction, Nonparametric C- and D-vine-based quantile regression, Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data, An embedded model estimator for non-stationary random functions using multiple secondary variables, ROC‐guided survival trees and ensembles, Forward variable selection for random forest models, HETEROGENEOUS TREATMENT EFFECTS OF NUDGE AND REBATE: CAUSAL MACHINE LEARNING IN A FIELD EXPERIMENT ON ELECTRICITY CONSERVATION, Semiparametric estimation of long-term treatment effects, Is there a role for statistics in artificial intelligence?, Distributional (Single) Index Models, Unnamed Item, Unnamed Item, SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting, Orthogonal statistical learning, A reluctant additive model framework for interpretable nonlinear individualized treatment rules, Gradient boosting for extreme quantile regression, Methods for integrating trials and non-experimental data to examine treatment effect heterogeneity, A conditional linear combination test with many weak instruments, Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors, The Effect of Job Loss and Unemployment Insurance on Crime in Brazil, Robust estimation of heterogeneous treatment effects: an algorithm-based approach, Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing, Designing optimal, data-driven policies from multisite randomized trials, Using Wasserstein generative adversarial networks for the design of Monte Carlo simulations, Quantile regression by dyadic CART, A distance-based test of independence between two multivariate time series, Minimax optimal rates for Mondrian trees and forests, Comment: Invariance and causal inference, Generalized random forests, Cross-Validation, Risk Estimation, and Model Selection: Comment on a Paper by Rosset and Tibshirani, Targeting customers under response-dependent costs, Unnamed Item, Predictive Distribution Modeling Using Transformation Forests, Learning causal effect using machine learning with application to China's typhoon, Experimental Evaluation of Individualized Treatment Rules, Regularizing Double Machine Learning in Partially Linear Endogenous Models, Distributional regression forests for probabilistic precipitation forecasting in complex terrain, Response transformation and profit decomposition for revenue uplift modeling, Two-stage least squares random forests with an application to Angrist and Evans (1998), Doubly robust treatment effect estimation with missing attributes, Transformation boosting machines, Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion), Bayesian additive regression trees with model trees, grf, Uncertainty quantification for honest regression trees, Augmented minimax linear estimation, Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning, Towards convergence rate analysis of random forests for classification, A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity, Learning When-to-Treat Policies, To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates, Bounds on the conditional and average treatment effect with unobserved confounding factors, Asymptotic properties of high-dimensional random forests

Uses Software

Cites Work