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In this paper, we propose a robust method to estimate the average treatment effects in observational studies when the number of potential confounders is possibly much greater than the sample size.
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Doubly robust estimation in missing data and causal inference models
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Imbens, G. W · 2005
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Rubin, D. B · 2006
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Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data
Kang, J. D · 2007
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Comment: Performance of double-robust estimators when” inverse probability” weights are highly variable
Robins, J · 2007
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Reconsidering the effects of education on political participation
Kam, C. D · 2008
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For objective causal inference, design trumps analysis
Rubin, D. B · 2008
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Simultaneous analysis of lasso and dantzig selector
Bickel, P. J · 2009
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Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data
Cao, W · 2009
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Bühlmann, P · 2015
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Confidence intervals for high-dimensional linear regression: Minimax rates and adaptivity
Cai, T. T · 2015
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Robust inference on average treatment effects with possibly more covariates than observations
Farrell, M. H · 2015
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Bias-reduced doubly robust estimation
Vermeulen, K · 2015
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Stable weights that balance covariates for estimation with incomplete outcome data
Zubizarreta, J. R · 2015
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High-dimensional propensity score adjustment in studies of treatment effects using health care claims data
Schneeweiss, S · 2009
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Bounded, efficient and doubly robust estimation with inverse weighting
Tan, Z · 2010
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Targeted learning: causal inference for observational and experimental data
Van der Laan, M. J · 2011
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Inverse probability tilting for moment condition models with missing data
Graham, B. S · 2012
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Entropy balancing for causal effects: Multivariate reweighting method to produce balanced samples in observational studies
Hainmueller, J · 2012
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Confidence intervals and hypothesis testing for high-dimensional regression
Javanmard, A · 2013
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Athey, S · 2016
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Post-selection inference for generalized linear models with many controls
Belloni, A · 2016
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Globally efficient nonparametric inference of average treatment effects by empirical balancing calibration weighting
Chan, K. C. G · 2016
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Double machine learning for treatment and causal parameters
Chernozhukov, V · 2016
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Chetverikov, D · 2016
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Improving covariate balancing propensity score: A doubly robust and efficient approach
Fan, J · 2016
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Covariate balancing propensity score by tailored loss functions
Zhao, Q · 2016
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A general theory of hypothesis tests and confidence regions for sparse high dimensional models
Ning, Y · 2017
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Tan, Z · 2017
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High-dimensional doubly robust tests for regression parameters
Dukes, O · 2018
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CBPS: R package for covariate balancing propensity score
Fong, C · 2018
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