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We investigate the finite sample performance of sample splitting, cross-fitting and averaging for the estimation of the conditional average treatment effect.
Randomization analysis of experimental data: The fisher randomization test comment
Donald B Rubin · 1980
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On adaptive estimation
Peter J Bickel · 1982
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On asymptotically efficient estimation in semiparametric models
Anton Schick · 1986
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Root-n-consistent semiparametric regression
Peter M Robinson · 1988
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Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study
Jared K Lunceford and Marie Davidian · 2004
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Super learning
Eric C Polley, Sherri Rose, and Mark J Van der Laan · 2011
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New statistical approaches to semiparametric regression with application to air pollution research
James M Robins, Peng Zhang, Rajeev Ayyagari, Roger Logan, Eric Tchetgen Tchetgen, Lingling Li, Thomas Lumley, and Aad van der Vaart · 2013
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Locally robust semiparametric estimation
Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K Newey, and James M Robins · 2016
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Susan Athey and Stefan Wager · 2017
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Quasi-oracle estimation of heterogeneous treatment effects
Xinkun Nie and Stefan Wager · 2017
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On the multiply robust estimation of the mean of the g-functional
Andrea Rotnitzky, James Robins, and Lucia Babino · 2017
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Cross-fitting and fast remainder rates for semiparametric estimation
Estimation of conditional average treatment effects with high-dimensional data
Qingliang Fan, Yu-Chin Hsu, Robert P Lieli, and Yichong Zhang · 2019
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Metalearners for estimating heterogeneous treatment effects using machine learning
Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu · 2019
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Nonparametric estimation of causal heterogeneity under high-dimensional confounding
Michael Zimmert and Michael Lechner · 2019
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Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects
P. Richard Hahn, Jared S. Murray, and Carlos M. Carvalho · 2020
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Optimal doubly robust estimation of heterogeneous causal effects
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Whitney K Newey and James R Robins · 2018
Cited alongside, same era.
Some methods for heterogeneous treatment effect estimation in high dimensions
Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H Shah, Trevor Hastie, and Robert Tibshirani · 2018
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Generalized random forests
Susan Athey, Stefan Wager, and Julie Tibshirani · 2019
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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
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Generic machine learning inference on heterogenous treatment effects in randomized experiments
Victor Chernozhukov, Mert Demirer, Esther Duflo, and Ivan Fernandez-Val
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Edward H Kennedy · 2020
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Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence
Michael C Knaus, Michael Lechner, and Anthony Strittmatter · 2020
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Machine learning for causal inference: on the use of cross-fit estimators
Paul N Zivich and Alexander Breskin · 2020
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