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Treatment effect estimation under unconfoundedness is a fundamental task in causal inference.
Sparsity double robust inference of average treatment effects
Jelena Bradic, Stefan Wager, and Yinchu Zhu · 1905
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Minimax semiparametric learning with approximate sparsity
Jelena Bradic, Victor Chernozhukov, Whitney K Newey, and Yinchu Zhu · 1912
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Daniel G Horvitz and Donovan J Thompson · 1952
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A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
James Robins · 1986
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Identification and estimation of local average treatment effects
Guido W Imbens and Joshua D Angrist · 1994
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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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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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James M Robins and Ya’acov Ritov · 1997
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On the role of the propensity score in efficient semiparametric estimation of average treatment effects
Jinyong Hahn · 1998
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Rejoinder
Daniel O Scharfstein, Andrea Rotnitzky, and James M Robins · 1999
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James M Robins and Andrea Rotnitzky · 2001
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Zhiqiang Tan · 2006
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Targeted maximum likelihood learning
Mark J van der Laan and Daniel Rubin · 2006
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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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Applications of influence functions to semiparametric regression models
Rajeev Ayyagari · 2010
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Square-root lasso: pivotal recovery of sparse signals via conic programming
Alexandre Belloni, Victor Chernozhukov, and Lie Wang · 2011
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Statistics for high-dimensional data: methods, theory and applications
Peter Bühlmann and Sara van de Geer · 2011
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Targeted learning: causal inference for observational and experimental data , volume 4
Mark J van der Laan and Sherri Rose · 2011
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Cross-validated targeted minimum-loss-based estimation
Wenjing Zheng 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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Pivotal estimation via square-root lasso in nonparametric regression
Alexandre Belloni, Victor Chernozhukov, and Lie Wang · 2014
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Kosuke Imai and Marc Ratkovic · 2014
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Confidence intervals and hypothesis testing for high-dimensional regression
Adel Javanmard and Andrea Montanari · 2014
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The Bayesian analysis of complex, high-dimensional models: Can it be CODA?
Ya’acov Ritov, Peter J Bickel, Anthony C Gamst, and Bastiaan Jan Korneel Kleijn · 2014
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On asymptotically optimal confidence regions and tests for high-dimensional models
Sara van de Geer, Peter Bühlmann, Ya’acov Ritov, and Ruben Dezeure · 2014
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Confidence intervals for low dimensional parameters in high dimensional linear models
Cun-Hui Zhang and Stephanie S Zhang · 2014
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Some new asymptotic theory for least squares series: Pointwise and uniform results
Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov, and Kengo Kato · 2015
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Robust inference on average treatment effects with possibly more covariates than observations
Max H Farrell · 2015
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Stable weights that balance covariates for estimation with incomplete outcome data
José R Zubizarreta · 2015
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Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting
Kwun Chuen Gary Chan, Sheung Chi Phillip Yam, and Zheng Zhang · 2016
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On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
Lin Liu, Rajarshi Mukherjee, and James M Robins · 2020
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Rajarshi Mukherjee and Subhabrata Sen · 2020
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Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
Yang Ning, Sida Peng, and Kosuke Imai · 2020
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High-dimensional inference for the average treatment effect under model misspecification using penalized bias-reduced double-robust estimation
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The balancing act in causal inference
Eli Ben-Michael, Avi Feller, David A Hirshberg, and José R Zubizarreta · 2021
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SLOPE is adaptive to unknown sparsity and asymptotically minimax
Weijie Su and Emmanuel Candes · 2016
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T Tony Cai and Zijian Guo · 2017
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Lin Liu, Rajarshi Mukherjee, Whitney K Newey, and James M Robins · 2017
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Outcome-adaptive lasso: variable selection for causal inference
Susan M Shortreed and Ashkan Ertefaie · 2017
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A generally efficient targeted minimum loss based estimator based on the highly adaptive lasso
Mark van der Laan · 2017
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Approximate residual balancing: debiased inference of average treatment effects in high dimensions
Susan Athey, Guido W Imbens, and Stefan Wager · 2018
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Pierre C Bellec, Guillaume Lecué, and Alexandre B Tsybakov · 2018
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Adaptive estimation of nonparametric functionals
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Characterization of parameters with a mixed bias property
Andrea Rotnitzky, Ezequiel Smucler, and James M Robins · 2021
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Kuanhao Jiang, Rajarshi Mukherjee, Subhabrata Sen, and Pragya Sur · 2022
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High-dimensional model-assisted inference for local average treatment effects with instrumental variables
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