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Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects.
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Random forests
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Estimation of direct causal effects
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Estimation of the effect of interventions that modify the received treatment
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Doubly robust policy evaluation and optimization
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Bounds for pure direct effect
Eric J. Tchetgen Tchetgen and Kelesitse Phiri · 2014
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Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data
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Targeted maximum likelihood learning
Mark J van der Laan and Daniel Rubin · 2006
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M.J. van der Laan, E. Polley, and A. Hubbard · 2007
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Jin Tian · 2008
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Mark J van der Laan and Maya L Petersen · 2008
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glmnet: Lasso and elastic-net regularized generalized linear models , 2009
Jerome Friedman, Trevor Hastie, and Rob Tibshirani · 2009
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Myth, Confusion, and Science in Causal Analysis
Judea Pearl · 2009
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Jessica G Young, Miguel A Hernán, and James M Robins · 2014
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Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov, and Ying Wei · 2015
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On partial identification of the pure direct effect
Caleb H Miles, Phyllis Kanki, Seema Meloni, and Eric J Tchetgen Tchetgen · 2015
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Ranger: a fast implementation of random forests for high dimensional data in c++ and r
Marvin N Wright and Andreas Ziegler · 2015
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The highly adaptive lasso estimator
David Benkeser and Mark van der Laan · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Double machine learning for treatment and causal parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, et al · 2016
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Defining and estimating causal direct and indirect effects when setting the mediator to specific values is not feasible
Judith J Lok · 2016
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Robust and flexible estimation of stochastic mediation effects: a proposed method and example in a randomized trial setting
Kara E Rudolph, Oleg Sofrygin, Wenjing Zheng, and Mark J Van Der Laan · 2017
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A generally efficient targeted minimum loss based estimator based on the highly adaptive lasso
Mark J van der Laan · 2017
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Interventional effects for mediation analysis with multiple mediators
Stijn Vansteelandt and Rhian M Daniel · 2017
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Longitudinal mediation analysis with time-varying mediators and exposures, with application to survival outcomes
Wenjing Zheng and Mark van der Laan · 2017
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Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins · 2018
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hal9001: The Scalable Highly Adaptive LASSO , 2018
Jeremy R Coyle and Nima S Hejazi · 2018
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sl3: Modern pipelines for machine learning and Super Learning
Jeremy R Coyle, Nima S Hejazi, Ivana Malenica, and Oleg Sofrygin · 2018
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Stochastic treatment regimes
Iván Díaz and Mark J van der Laan · 2018
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Highly adaptive lasso (hal)
Mark J van der Laan and David Benkeser · 2018
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Targeted Learning in Data Science: Causal Inference for Complex longitudinal Studies
Mark J van der Laan and Sherri Rose · 2018
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medshift: Causal mediation analysis for stochastic interventions in R , 2019
Nima S Hejazi and Iván Díaz · 2019
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Causal organic direct and indirect effects: closer to baron and kenny
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R: A Language and Environment for Statistical Computing
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