Solving Heterogeneous Estimating Equations with Gradient Forests
Explore this paper's citation graph
Summary
This paper develops a unified framework for the design of fast tree-growing procedures for tasks that can be characterized by heterogeneous estimating equations, and proves the consistency of gradient forests, and establishes a central limit theorem.
- Type
- preprint
- Published
- 2016-10-01
- Cited by
- 20
- References
- 72
- Access
- Open access
- OpenAlex
- https://openalex.org/W2529000336
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1168129
Keywords
Random forest, Computer science, Inference, Quantile, Statistical inference
References
- Tree-structured survival analysis.
- Do Retirement Saving Programs Increase Saving
- Program evaluation with high-dimensional data
- Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records
- Quantile Regression Forests
- Greedy function approximation: A gradient boosting machine.
- Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size
- Optimal weighted nearest neighbour classifiers
- On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification
- Generalized M‐fluctuation tests for parameter instability
- Convergence of Newton's method and inverse function theorem in Banach space
- Toward a curse of dimensionality appropriate (CODA) asymptotic theory for semi-parametric models.
- Local maximum likelihood estimation and inference
- The Influence Curve and Its Role in Robust Estimation
- The Jackknife Estimate of Variance
- Local Likelihood Estimation
- The Kernel Estimate of a Regression Function in Likelihood-Based Models
- A review of survival trees
- Estimation and Accuracy after Model Selection
- Tree-based multivariate regression and density estimation with right-censored data
Cited by
- Some methods for heterogeneous treatment effect estimation in high dimensions
- Beyond prediction: Using big data for policy problems
- Efficient Policy Learning
- Causal Inference through the Method of Direct Estimation
- Using Machine Learning to Explain Violations of the 'Law of One Price'
- Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting
- Causal tree with instrumental variable: an extension of the causal tree framework to irregular assignment mechanisms
- Peaking Interest: How Awareness Drives the Effectiveness of Time-of-Use Electricity Pricing
- Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors
- Resolving Simultaneity Bias: Using Features to Estimate Causal Effects in Competitive Games
- Fairness in College Admission Exams: From Test Score Gaps to Earnings Equality
- Fulfillment by Amazon versus fulfillment by seller: An interpretable risk‐adjusted fulfillment model
- Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability
- Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression
- BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
- Estimation of a treatment effect based on a modified covariates method with L0 norm
- Heterogeneous Treatment Effects of Medicaid and Efficient Policies: Evidence from the Oregon Health Insurance Experiment
- USING MACHINE LEARNING TO PREDICT PRICE DISPERSION
- USING MACHINE LEARNING TO EXPLAIN PRICE DISPERSION
- NBER WORKING PAPER SERIES THE WELFARE EFFECTS OF NUDGES: A CASE STUDY OF ENERGY USE SOCIAL COMPARISONS Hunt Allcott
Related papers
- Sequential Regression Trees for Learning and Design
- Trees-Based Models for Correlated Data
- Regression-Enhanced Random Forests
- Optimization of Random Forest Based Methods Applying the Genetic Algorithms
- Tree regression models using statistical testing and mixed integer programming
- VSURF: An R Package for Variable Selection Using Random Forests