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Motivated by applications in precision medicine and treatment effect heterogeneity, recent research has focused on estimating conditional average treatment effects (CATEs) using machine learning (ML).
When some of regression coefficients estimation regressors are not always observed
Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994) · 1994
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A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter
Hammer, S. M., Katzenstein, D. A., Hughes, M. D., Gundacker, H., Schooley, R. T., Haubrich, R. H., Henry, W. K., Lederman, M. M., Phair, J. P., Niu, M., Hirsch, M. S., and Merigan, T. C. (1996) · 1996
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Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2
Slamon, D. J., Leyland-Jones, B., Shak, S., Fuchs, H., Paton, V., Bajamonde, A., Fleming, T., Eiermann, W., Wolter, J., Pegram, M., Baselga, J., and Norton, L. (2001) · 2001
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Optimal dynamic treatment regimes
Murphy, S. A. (2003) · 2003
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gam: Generalized additive models
Hastie, T. (2004) · 2004
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Subgroup analysis in randomised controlled trials: importance, indications, and interpretation
Rothwell, P. M. (2005) · 2005
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Super learner
van der Laan, M. J., Polley, E. C., and Hubbard, A. E. (2007) · 2007
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Floodgate: inference for model-free variable importance
Zhang, L. and Janson, L. (2020) · 2007
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Variable importance assessment in regression: linear regression versus random forest
Grömping, U. (2009) · 2009
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Targeted learning of an optimal dynamic treatment, and statistical inference for its mean outcome
van der Laan, M. J. (2013) · 2013
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Targeted learning of the mean outcome under an optimal dynamic treatment rule
van der Laan, M. J. and Luedtke, A. R. (2014) · 2014
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Bias-reduced doubly robust estimation
Vermeulen, K. and Vansteelandt, S. (2015) · 2015
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Using decision lists to construct interpretable and parsimonious treatment regimes
Zhang, Y., Laber, E. B., Tsiatis, A., and Davidian, M. (2015) · 2015
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Recursive partitioning for heterogeneous causal effects
Athey, S. and Imbens, G. (2016) · 2016
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Randomization inference for treatment effect variation
Ding, P., Feller, A., and Miratrix, L. (2016) · 2016
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Super-learning of an optimal dynamic treatment rule
Luedtke, A. R. and van der Laan, M. J. (2016) · 2016
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One-step targeted minimum loss-based estimation based on universal least favorable one-dimensional submodels
van der Laan, M. J. and Gruber, S. (2016) · 2016
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Smoothing parameter and model selection for general smooth models
Wood, S. N., Pya, N., and Säfken, B. (2016) · 2016
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On Shapley value for measuring importance of dependent inputs
Owen, A. B. and Prieur, C. (2017) · 2017
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Higher-Order Targeted Loss-Based Estimation
Carone, M., Díaz, I., and van der Laan, M. J. (2018) · 2018
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Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. K., and Robins, J. M. (2018) · 2018
Policy learning with observational data
Athey, S. and Wager, S. (2021) · 2021
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A fundamental measure of treatment effect heterogeneity
Levy, J., van der Laan, M. J., Hubbard, A., and Pirracchio, R. (2021) · 2021
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Nonparametric variable importance assessment using machine learning techniques
Williamson, B. D., Gilbert, P. B., Carone, M., and Simon, N. R. (2021) · 2021
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Model free variable importance for high dimensional data
Hama, N., Mase, M., and Owen, A. B. (2022) · 2022
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Demystifying statistical learning based on efficient influence functions
Hines, O., Dukes, O., Diaz-Ordaz, K., and Vansteelandt, S. (2022) · 2022
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Cross-validated targeted minimum-loss-based estimation
Zheng, W. and van der Laan, M. J. (2011) · 2022
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Distribution-free predictive inference for regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018) · 2018
Cited alongside, same era.
Kernel smoothing of the treatment effect CDF
Levy, J. and van der Laan, M. J. (2018) · 2018
Cited alongside, same era.
Cross-fitting and fast remainder rates for semiparametric estimation
Newey, W. K. and Robins, J. M. (2018) · 2018
Cited alongside, same era.
Estimation and inference of heterogeneous treatment effects using random forests
Wager, S. and Athey, S. (2018) · 2018
Cited alongside, same era.
Generalized random forests
Athey, S., Tibshirani, J., and Wager, S. (2019) · 2019
Cited alongside, same era.
Metalearners for estimating heterogeneous treatment effects using machine learning
Künzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B. (2019) · 2019
Cited alongside, same era.
A Flexible Approach for Predictive Biomarker Discovery
Boileau, P., Qi, N. T., van der Laan, M. J., Dudoit, S., and Leng, N. (2023) · 2023
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Optimally weighted average derivative effects
Hines, O., Diaz-Ordaz, K., and Vansteelandt, S. (2023) · 2023
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Nonparametric inference on non-negative dissimilarity measures at the boundary of the parameter space
Hudson, A. (2023) · 2023
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Towards optimal doubly robust estimation of heterogeneous causal effects
Kennedy, E. H. (2023) · 2023
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Targeted learning on variable importance measure for heterogeneous treatment effect
Li, H., Hubbard, A., and van der Laan, M. J. (2023) · 2023
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A general framework for inference on algorithm-agnostic variable importance
Williamson, B. D., Gilbert, P. B., Simon, N. R., and Carone, M. (2023) · 2023
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Rank-transformed subsampling: inference for multiple data splitting and exchangeable p
Guo, F. R. and Shah, R. D. (2025) · 2025
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Nonparametric Assessment of Variable Selection and Ranking Algorithms
Tang, Z. and Westling, T. (2025) · 2025
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