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Higher-Order Influence Functions (HOIF), developed in a series of papers over the past twenty years, are a fundamental theoretical device for constructing rate-optimal causal-effect estimators from observational studies.
Limiting distributions in simple random sampling from a finite population
Jaroslav Hájek · 1960
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Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial
Stuart J Pocock and Richard Simon · 1975
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Confidence intervals for causal parameters
James M Robins · 1988
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A class of U {U} -statistics and asymptotic normality of the number of k k -clusters
Rabi N Bhattacharya and Jayanta K Ghosh · 1992
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Estimation of regression coefficients when some regressors are not always observed
James M Robins, Andrea Rotnitzky, and Lue Ping Zhao · 1994
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Efficiency study of estimators for a treatment effect in a pretest–posttest trial
Li Yang and Anastasios A Tsiatis · 2001
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Covariance adjustment in randomized experiments and observational studies: Comment
James M Robins · 2002
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Covariance adjustment in randomized experiments and observational studies
Paul R Rosenbaum · 2002
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The NLopt nonlinear-optimization package, 2007
Steven G Johnson · 2007
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Higher order influence functions and minimax estimation of nonlinear functionals
James Robins, Lingling Li, Eric Tchetgen Tchetgen, and Aad van der Vaart · 2008
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Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approach
Anastasios A Tsiatis, Marie Davidian, Min Zhang, and Xiaomin Lu · 2008
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Improving efficiency of inferences in randomized clinical trials using auxiliary covariates
Min Zhang, Anastasios A Tsiatis, and Marie Davidian · 2008
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Covariate adjustment in randomized trials with binary outcomes: Targeted maximum likelihood estimation
Kelly L Moore and Mark J van der Laan · 2009
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Rerandomization to improve covariate balance in experiments
Kari Lock Morgan and Donald B Rubin · 2012
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Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique
Winston Lin · 2013
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Causal etiology of the research of James M. Robins
Thomas S Richardson and Andrea Rotnitzky · 2014
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Sharp bounds on the variance in randomized experiments
Peter M Aronow, Donald P Green, and Donald KK Lee · 2014
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An introduction to matrix concentration inequalities
Joel A Tropp · 2015
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An introduction to matrix concentration inequalities
Joel A Tropp · 2015
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Semiparametric efficient empirical higher order influence function estimators
Lin Liu, Rajarshi Mukherjee, Whitney K Newey, and James M Robins · 2017
New n \sqrt{n} -consistent, numerically stable empirical higher-order influence function estimators
Lin Liu and Chang Li · 2023
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Adjusting for covariates in randomized clinical trials for drugs and biological products, 2023
US Food and Drug Administration · 2023
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Model-robust inference for clinical trials that improve precision by stratified randomization and covariate adjustment
Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, and Michael Rosenblum · 2023
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Toward better practice of covariate adjustment in analyzing randomized clinical trials
Ting Ye, Jun Shao, Yanyao Yi, and Qingyuan Zhao · 2023
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Exact bias correction for linear adjustment of randomized controlled trials
Haoge Chang, Joel A Middleton, and Peter M Aronow · 2024
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The LOOP estimator: Adjusting for covariates in randomized experiments
Edward Wu and Johann A Gagnon-Bartsch · 2018
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Statistical inference for covariate-adaptive randomization procedures
Wei Ma, Yichen Qin, Yang Li, and Feifang Hu · 2020
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Regression adjustment in completely randomized experiments with a diverging number of covariates
Lihua Lei and Peng Ding · 2021
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Covariate-adjusted Fisher randomization tests for the average treatment effect
Anqi Zhao and Peng Ding · 2021
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Regression adjustment in completely randomized experiments with a diverging number of covariates
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A design-based Riesz representation framework for randomized experiments
Christopher Harshaw, Yitan Wang, and Fredrik Savje · 2022
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A new and unified family of covariate adaptive randomization procedures and their properties
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Using Wasserstein generative adversarial networks for the design of Monte Carlo simulations
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Unbiased regression-adjusted estimation of average treatment effects in randomized controlled trials
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Nonparametric identification is not enough, but randomized controlled trials are
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A general form of covariate adjustment in clinical trials under covariate-adaptive randomization
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Adjustments with many regressors under covariate-adaptive randomizations
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Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
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Neumann-series corrections for regression adjustment in randomized experiments
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Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
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