When is the estimated propensity score better? High-dimensional analysis and bias correction

A generalization of sampling without replacement from a finite universe

D. G. Horvitz and D. J. Thompson · 1952

Earlier work this paper cites.

Some applications of the metric entropy condition to harmonic analysis

G. Pisier · 1983

Earlier work this paper cites.

The central role of the propensity score in observational studies for causal effects

P. R. Rosenbaum and D. B. Rubin · 1983

Earlier work this paper cites.

Reducing bias in observational studies using subclassification on the propensity score

P. R. Rosenbaum and D. B. Rubin · 1984

Earlier work this paper cites.

Constructing a control group using multivariate matched sampling methods that incorporate the propensity score

P. R. Rosenbaum and D. B. Rubin · 1985

Earlier work this paper cites.

Model-based direct adjustment

P. R. Rosenbaum · 1987

Earlier work this paper cites.

Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity

S. Portnoy · 1988

Earlier work this paper cites.

Estimating exposure effects by modelling the expectation of exposure conditional on confounders

J. M. Robins, S. D. Mark, and W. K. Newey · 1992

Earlier work this paper cites.

Characterizing the effect of matching using linear propensity score methods with normal distributions

D. B. Rubin and N. Thomas · 1992

Earlier work this paper cites.

Limit theorems for U-processes

M. A. Arcones and E. Giné · 1993

Earlier work this paper cites.

Estimation of regression coefficients when some regressors are not always observed

James M Robins, Andrea Rotnitzky, and Lue Ping Zhao · 1994

Cited alongside, same era.

Weak convergence and Empirical processes

A. W. van der Vaart and J. A Wellner · 1996

Cited alongside, same era.

On the role of the propensity score in efficient semiparametric estimation of average treatment effects

J. Hahn · 1998

Cited alongside, same era.

Efficient estimation of average treatment effects using the estimated propensity score

K. Hirano, G. W Imbens, and G. Ridder · 2003

Cited alongside, same era.

A paradox concerning nuisance parameters and projected estimating functions

M. Henmi and S. Eguchi · 2004

Cited alongside, same era.

A tail inequality for suprema of unbounded empirical processes with applications to Markov chains

Double/debiased machine learning for treatment and structural parameters

V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, and J. Robins · 2018

Later among the works it cites.

Regression adjustment in completely randomized experiments with a diverging number of covariates

Original

L. Lei and P. Ding · 2018

Later among the works it cites.

Minimax semiparametric learning with approximate sparsity

Original

J. Bradic, V. Chernozhukov, W. K. Newey, and Y. Zhu · 2019

Later among the works it cites.

Two-step estimation and inference with possibly many included covariates

M. D. Cattaneo, M. Jansson, and X. Ma · 2019

Later among the works it cites.

High-dimensional statistics: A non-asymptotic viewpoint

M. J. Wainwright · 2019

Later among the works it cites.