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Causal random forests provide efficient estimates of heterogeneous treatment effects.
Sensitivity estimates for nonlinear mathematical models
I.M. Sobol · 1993
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Quantile regression forests
N. Meinshausen · 2006
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Four types of effect modification: a classification based on directed acyclic graphs
T.J. VanderWeele and J.M. Robins · 2007
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Bayesian nonparametric modeling for causal inference
J.L. Hill · 2011
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Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees
D.P. Green and H.L. Kern · 2012
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Epidemiology: an introduction
K.J. Rothman · 2012
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Estimating individualized treatment rules using outcome weighted learning
Y. Zhao, D. Zeng, A.J. Rush, and M.R. Kosorok · 2012
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Estimating treatment effect heterogeneity in randomized program evaluation
I. Kosuke and R. Marc · 2013
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Consistency of random forests
E. Scornet, G. Biau, and J.-P. Vert · 2015
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Batch learning from logged bandit feedback through counterfactual risk minimization
A. Swaminathan and T. Joachims · 2015
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Predicting the future—big data, machine learning, and clinical medicine
Z. Obermeyer and E.J. Emanuel · 2016
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Estimating individual treatment effect: generalization bounds and algorithms
U. Shalit, F.D. Johansson, and D. Sontag · 2017
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Who should be treated? empirical welfare maximization methods for treatment choice
T. Kitagawa and A. Tetenov · 2018
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Distribution-free predictive inference for regression
J. Lei, M. G’Sell, A. Rinaldo, R.J. Tibshirani, and L. Wasserman · 2018
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Estimation and inference of heterogeneous treatment effects using random forests
S. Wager and S. Athey · 2018
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Estimating treatment effects with causal forests: An application
S. Athey and S. Wager · 2019
Policy learning with observational data
S. Athey and S. Wager · 2021
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Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance
G. Hooker, L. Mentch, and S. Zhou · 2021
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Quasi-oracle estimation of heterogeneous treatment effects
X. Nie and S. Wager · 2021
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A unified approach for inference on algorithm-agnostic variable importance
B.D. Williamson, P.B. Gilbert, N.R. Simon, and M. Carone · 2021
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Mean decrease accuracy for random forests: inconsistency, and a practical solution via the sobol-mda
C. Bénard, S. Da Veiga, and E. Scornet · 2022
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Generalized random forests
S. Athey, J. Tibshirani, and S. Wager · 2019
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Metalearners for estimating heterogeneous treatment effects using machine learning
S. Künzel, J.S. Sekhon, P.J. Bickel, and B. Yu · 2019
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Causal inference: What if. boca raton: Chapman & hill/crc
M.A. Hernan and J. Robins · 2020
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Optimal doubly robust estimation of heterogeneous causal effects
E.H. Kennedy · 2020
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P. Boileau, N.T. Qi, M.J. van der Laan, S. Dudoit, and N. Leng · 2022
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Variable importance measures for heterogeneous causal effects
O. Hines, K. Diaz-Ordaz, and S. Vansteelandt · 2022
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Risk ratio, odds ratio, risk difference… which causal measure is easier to generalize?
B. Colnet, J. Josse, G. Varoquaux, and E. Scornet · 2023
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grf: Generalized Random Forests. R package version 2.3.0
J. Tibshirani, S. Athey, E. Sverdrup, and S. Wager · 2023
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