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A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance.
Cross-validatory choice and assessment of statistical predictions
Mervyn Stone · 1974
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The predictive sample reuse method with applications
Seymour Geisser · 1975
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The Copeland method
Donald G Saari and Vincent R Merlin · 1996
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How to make a Hill plot
Holger Drees, Sidney Resnick, and Laurens de Haan · 2000
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No unbiased estimator of the variance of k-fold cross-validation
Yoshua Bengio and Yves Grandvalet · 2004
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Causal inference using potential outcomes: Design, modeling, decisions
Donald B Rubin · 2005
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Mostly Harmless Econometrics
Joshua D Angrist and Jörn-Steffen Pischke · 2008
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Semiparametric exponential families for heavy-tailed data
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Causal Inference in Statistics, Social, and Biomedical Sciences
Guido W Imbens and Donald B Rubin · 2015
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Recursive partitioning for heterogeneous causal effects
Susan Athey and Guido Imbens · 2016
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Scalable semiparametric inference for the means of heavy-tailed distributions
Matt Taddy, Hedibert Freitas Lopes, and Matt Gardner · 2016
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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
A comparison of methods for model selection when estimating individual treatment effects
Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah · 2018
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Cross-validation confidence intervals for test error
Pierre Bayle, Alexandre Bayle, Lucas Janson, and Lester Mackey · 2020
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Ml-based causal inference tutorial
Vitor Hadad · 2020
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Causal Inference: What If
Miguel Hernan and James Robins · 2020
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Cross-validation with confidence
Jing Lei · 2020
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Stats 361: Causal inference
Stefan Wager · 2020
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Cited alongside, same era.