Pages that link to "Item:Q4559704"
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The following pages link to Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (Q4559704):
Displaying 50 items.
- Decomposing Treatment Effect Variation (Q75398) (← links)
- Permutation test for heterogeneous treatment effects with a nuisance parameter (Q95381) (← links)
- Experimental Evaluation of Individualized Treatment Rules (Q112156) (← links)
- Estimating treatment effect heterogeneity in randomized program evaluation (Q142565) (← links)
- Generalized random forests (Q666599) (← links)
- Mathematical optimization in classification and regression trees (Q828748) (← links)
- Semiparametric estimation for average causal effects using propensity score-based spline (Q830691) (← links)
- Individual-level social influence identification in social media: a learning-simulation coordinated method (Q1631522) (← links)
- Learning causal effect using machine learning with application to China's typhoon (Q2023742) (← links)
- (Machine) learning parameter regions (Q2024444) (← links)
- Adversarial balancing-based representation learning for causal effect inference with observational data (Q2036786) (← links)
- Doubly robust treatment effect estimation with missing attributes (Q2044265) (← links)
- Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion) (Q2057337) (← links)
- The designed bootstrap for causal inference in big observational data (Q2063871) (← links)
- Continuous treatment effect estimation via generative adversarial de-confounding (Q2066652) (← links)
- Partially observable environment estimation with uplift inference for reinforcement learning based recommendation (Q2071406) (← links)
- Uncertainty quantification for honest regression trees (Q2072416) (← links)
- Uncertainty quantification for Bayesian CART (Q2073718) (← links)
- An omnibus test for detection of subgroup treatment effects via data partitioning (Q2080743) (← links)
- Beyond the mean: a flexible framework for studying causal effects using linear models (Q2088915) (← links)
- Towards convergence rate analysis of random forests for classification (Q2093392) (← links)
- To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates (Q2098058) (← links)
- Optimal policy trees (Q2102338) (← links)
- Bounds on the conditional and average treatment effect with unobserved confounding factors (Q2105186) (← links)
- Random forest estimation of conditional distribution functions and conditional quantiles (Q2106811) (← links)
- Asymptotic properties of high-dimensional random forests (Q2112821) (← links)
- Nonparametric feature selection by random forests and deep neural networks (Q2129580) (← links)
- Recent advances in statistical methodologies in evaluating program for high-dimensional data (Q2132738) (← links)
- Robust causal inference for incremental return on ad spend with randomized paired geo experiments (Q2135326) (← links)
- Subgroup identification and variable selection for treatment decision making (Q2135330) (← links)
- Rates of convergence for random forests via generalized U-statistics (Q2136608) (← links)
- Robust machine learning for treatment effects in multilevel observational studies under cluster-level unmeasured confounding (Q2141660) (← links)
- Sufficient dimension reduction for average causal effect estimation (Q2147407) (← links)
- Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon Health Insurance Experiment (Q2154220) (← links)
- Augmented direct learning for conditional average treatment effect estimation with double robustness (Q2154959) (← links)
- Nonlinear predictive directions in clinical trials (Q2157508) (← links)
- Estimation of partially conditional average treatment effect by double kernel-covariate balancing (Q2168086) (← links)
- Estimating causal effects with optimization-based methods: a review and empirical comparison (Q2171584) (← links)
- Modeling of time series using random forests: theoretical developments (Q2209824) (← links)
- Minimax optimal rates for Mondrian trees and forests (Q2215734) (← links)
- Semiparametric Bayesian causal inference (Q2215769) (← links)
- Invariance, causality and robustness (Q2218071) (← links)
- Rejoinder: Invariance, causality and robustness (Q2218074) (← links)
- On IPW-based estimation of conditional average treatment effects (Q2242843) (← links)
- Targeted smooth Bayesian causal forests: an analysis of heterogeneous treatment effects for simultaneous vs. interval medical abortion regimens over gestation (Q2247463) (← links)
- Consistent estimation of residual variance with random forest out-of-bag errors (Q2322623) (← links)
- Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition (Q2325609) (← links)
- Non-separable models with high-dimensional data (Q2330742) (← links)
- Constructing effective personalized policies using counterfactual inference from biased data sets with many features (Q2425241) (← links)
- Statistical inference of heterogeneous treatment effect based on single-index model (Q2674496) (← links)
