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Inverse probability weighting (IPW) is a general tool in survey sampling and causal inference, used both in Horvitz-Thompson estimators, which normalize by the sample size, and H\'ajek/self-normalized estimators, which normalize by the sum of the inverse probability weights.
A generalization of sampling without replacement from a finite universe
D. G. Horvitz and D. J. Thompson · 1952
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Conditional monte carlo for normal samples
Hale F. Trotter and John W. Tukey · 1954
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Monte Carlo Methods
J. M. Hammersley and D. C. Handscomb · 1964
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Admissibility and bayes estimation in sampling finite populations. i
V. P. Godambe and V. M. Joshi · 1965
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An essay on the logical foundations of survey sampling, part i
D Basu · 1971
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Some results on generalized difference estimation and generalized regression estimation for finite populations
Claes M. Cassel, Carl E. Sarndal, and Jan H. Wretman · 1976
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On the convergence of the horvitz-thompson estimator
P. M. Robinson · 1982
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Estimating a finite population mean from unequal probability samples
Roderick J. A. Little · 1983
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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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Weighted average importance sampling and defensive mixture distributions
Tim Hesterberg · 1995
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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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Model assisted survey sampling
Carl-Erik Särndal, Bengt Swensson, and Jan Wretman · 2003
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Nonparametric estimation of average treatment effects under exogeneity: A review
Guido W Imbens · 2004
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Statistical treatment rules for heterogeneous populations
Charles F Manski · 2004
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Improving semiparametric estimation by using surrogate data
Song Xi Chen, Denis H. Y. Leung, and Jing Qin · 2008
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Empirical likelihood in missing data problems
Jing Qin, Biao Zhang, and Denis H. Y. Leung · 2009
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On improved estimation for importance sampling
David Firth · 2011
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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 · 2018
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Who should be treated? empirical welfare maximization methods for treatment choice
Toru Kitagawa and Aleksey Tetenov · 2018
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Confidence intervals for policy evaluation in adaptive experiments
Vitor Hadad, David A Hirshberg, Ruohan Zhan, Stefan Wager, and Susan Athey · 2019
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Consistency of the horvitz–thompson estimator under general sampling and experimental designs
Angèle Delevoye and Fredrik Sävje · 2020
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Policy learning with observational data
Susan Athey and Stefan Wager · 2021
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