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A new method for estimating the conditional average treatment effect is proposed in the paper.
On estimating regression
E.A. Nadaraya · 1964
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L. Breiman · 2001
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Stochastic gradient boosting
J.H. Friedman · 2002
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Nonparametric estimation of average treatment effects under exogeneity: A review
G.W. Imbens · 2004
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Nonparametric estimation of average treatment effects under exogeneity: A review
G.W. Imbens · 2004
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Causal inference using potential outcomes: Design, modeling, decisions
D.B. Rubin · 2005
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L2 boosting in kernel regression
B.U. Park, Y.K. Lee, and S. Ha · 2009
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A survey on transfer learning
S. Pan and Q. Yang · 2010
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Heterogeneous treatment effects: What does a regression estimate?
W. Rhodes · 2010
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A non-linear function approximation from small samples based on nadaraya-watson kernel regression
M.I. Shapiai, Z. Ibrahim, M. Khalid, Lee Wen Jau, and V. Pavlovich · 2010
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Bayesian nonparametric modeling for causal inference
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J.A.K. Suykens K. De Brabanter, J. De Brabanter and B. De Moor · 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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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 heterogeneous treatment effects with observational data
Y. Xie, J.E. Brand, and B. Jann · 2012
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Bandwidth selection for Nadaraya-Watson kernel estimator using cross-validation based on different penalty functions
Yumin Zhang · 2014
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Estimation and inference of heterogeneous treatment effects using random forests
S. Wager and S. Athey · 2015
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Transfer learning using computational intelligence: A survey
J. Lu, V. Behbood, P. Hao, H. Zuo, S. Xue, and G. Zhang · 2015
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Learning to personalize from observational data
N. Kallus · 2016
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Recursive partitioning for heterogeneous causal effects
S. Athey and G. Imbens · 2016
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Concise summarization of heterogeneous treatment effect using total variation regularized regression
A. Deng, P. Zhang, S. Chen, D.W. Kim, and J. Lu · 2016
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A survey of transfer learning
K. Weiss, T.M. Khoshgoftaar, and D. Wang · 2016
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Solving heterogeneous estimating equations with gradient forests
S. Athey, J. Tibshirani, and S. Wager · 2016
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Learning optimal individualized treatment rules from electronic health record data
Y. Wang, P. Wu, Y. Liu, C. Weng, and D. Zeng · 2016
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The mean shift algorithm and its relation to kernel regression
Y.A. Ghassabeh and F. Rudzicz · 2016
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Estimating individual treatment effect in observational data using random forest methods
M. Lu, S. Sadiq, D.J. Feaster, and H. Ishwaran · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
U. Shalit, F.D. Johansson, and D.A. Sontag · 2017
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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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S. Powers, J. Qian, K. Jung, A. Schuler, N.H. Shah, T. Hastie, and R. Tibshirani · 2017
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Residual weighted learning for estimating individualized treatment rules
X. Zhou, N. Mayer-Hamblett, U. Khan, and M.R. Kosorok · 2017
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Mining heterogeneous causal effects for personalized cancer treatment
W. Zhang, T.D. Le, L. Liu, Z.-H. Zhou, and J. Li · 2017
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Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods
J. Grimmer, S. Messing, and S.J. Westwood · 2017
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Generative local metric learning for kernel regression
Y.-K. Noh, M. Sugiyama, K.-E. Kim, F. Park, and D.D. Lee · 2017
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Estimation and inference of heterogeneous treatment effects using random forests
S. Wager and S. Athey · 2017
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DNN: A two-scale distributional tale of heterogeneous treatment effect inference
Y. Fan, J. Lv, and J. Wang · 2018
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Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches
A.B. Szczotka, D.I. Shakir, D. Ravi, M.J. Clarkson, S.P. Pereira, and T. Vercauteren · 2020
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A fast approximation of the nadaraya-watson regression with the k-nearest neighbor crossover kernel
T. Ito, N. Hamada, K. Ohori, and H. Higuchi · 2020
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Heterogeneous treatment effect analysis based on machine-learning methodology
Xiajing Gong, Meng Hu, M. Basu, and Liang Zhao · 2021
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A short survey on forest based heterogeneous treatment effect estimation methods: Meta-learners and specific models
Hao Jiang, Peng Qi, Jingying Zhou, Jack Zhou, and Sharath Rao · 2021
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Evaluating treatment prioritization rules via rank-weighted average treatment effects
S. Yadlowsky, S. Fleming, N. Shah, E. Brunskill, and S. Wager · 2021
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S.R. Kunzel, B.C. Stadie, N. Vemuri, V. Ramakrishnan, J.S. Sekhon, and P. Abbeel · 2018
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Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases
T. Wendling, K. Jung, A. Callahan, A. Schuler, N.H. Shah, and B. Gallego · 2018
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Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design
Ahmed Alaa and Mihaela van der Schaar · 2018
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High-dimensional inference for personalized treatment decision
X.J. Jeng, W. Lu, and H. Peng · 2018
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S. Athey, J. Tibshirani, and S. Wager · 2018
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Y. Xie, N. Chen, and X. Shi · 2018
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Orthogonal random forest for heterogeneous treatment effect estimation
M. Oprescu, V. Syrgkanis, and Z.S. Wu · 2018
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Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms
A. Curth and M. van der Schaar · 2021
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Cetransformer: Casual effect estimation via transformer based representation learning
Zhenyu Guo, Shuai Zheng, Zhizhe Liu, Kun Yan, and Zhenfeng Zhu · 2021
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Deep learning: a statistical viewpoint
P.L. Bartlett, A. Montanari, and A. Rakhlin · 2021
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On inductive biases for heterogeneous treatment effect estimation
A. Curth and M. van der Schaar · 2021
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Dimension-free average treatment effect inference with deep neural networks
Xinze Du, Yingying Fan, Jinchi Lv, Tianshu Sun, and P. Vossler · 2021
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Vcnet and functional targeted regularization for learning causal effects of continuous treatments
Lizhen Nie, Mao Ye, Qiang Liu, and D. Nicolae · 2021
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Ncore: Neural counterfactual representation learning for combinations of treatments
S. Parbhoo, S. Bauer, and P. Schwab · 2021
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Budgeted heterogeneous treatment effect estimation
Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou · 2021
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Multi-source causal inference using control variates
Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, and M.I. Jordan · 2021
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Conditional distributional treatment effect with kernel conditional mean embeddings and u-statistic regression
J. Park, U. Shalit, B. Scholkopf, and K. Muandet · 2021
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Data-driven transient stability assessment based on kernel regression and distance metric learning
X. Liu, Y. Min, L. Chen, X. Zhang, and C. Feng · 2021
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On locality of local explanation models
S. Ghalebikesabi, L. Ter-Minassian, K. Diaz-Ordaz, and C. Holmes · 2021
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A. Zhang, Z.C. Lipton, M. Li, and A.J. Smola · 2021
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Heterogeneous treatment effects estimation: When machine learning meets multiple treatment regime
arXiv:2205.14714 · 2022
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Causal classification: Treatment effect estimation vs. outcome prediction
C. Fernandez-Loria and F. Provost · 2022
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Causal decision making and causal effect estimation are not the same…and why it matters
C. Fernandez-Loria and F. Provost · 2022
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Combining observational and randomized data for estimating heterogeneous treatment effects
T. Hatt, J. Berrevoets, A. Curth, S. Feuerriegel, and M. van der Schaar · 2022
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Integrative learner of heterogeneous treatment effects combining experimental and observational studies
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A unified survey of treatment effect heterogeneity modelling and uplift modelling
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Causal transformer for estimating counterfactual outcomes
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Can transformers be strong treatment effect estimators?
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Exploring transformer backbones for heterogeneous treatment effect estimation
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Efficient heterogeneous treatment effect estimation with multiple experiments and multiple outcomes
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Individual treatment effect estimation through controlled neural network training in two stages
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