Fetching the paper…
Reading the bibliography…
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML).
Minimax estimates of linear functionals in a hilbert space
P. Speckman · 1979
Earlier work this paper cites.
Randomization analysis of experimental data: The fisher randomization test comment
D. B. Rubin · 1980
Earlier work this paper cites.
Evaluating the econometric evaluations of training programs with experimental data
R. J. LaLonde · 1986
Earlier work this paper cites.
Calibration estimators in survey sampling
J.-C. Deville and C.-E. Särndal · 1992
Earlier work this paper cites.
Optimal plug-in estimators for nonparametric functional estimation
L. Goldstein and K. Messer · 1992
Earlier work this paper cites.
The asymptotic variance of semiparametric estimators
W. K. Newey · 1994
Earlier work this paper cites.
Estimation of regression coefficients when some regressors are not always observed
J. M. Robins, A. Rotnitzky, and L. P. Zhao · 1994
Earlier work this paper cites.
On methods of sieves and penalization
X. Shen · 1997
Earlier work this paper cites.
Least absolute shrinkage is equivalent to quadratic penalization
Y. Grandvalet · 1998
Earlier work this paper cites.
Undersmoothing and bias corrected functional estimation
W. K. Newey, F. Hsieh, and J. Robins · 1998
Earlier work this paper cites.
Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
R. H. Dehejia and S. Wahba · 1999
Earlier work this paper cites.
Imposing moment restrictions from auxiliary data by weighting
J. K. Hellerstein and G. W. Imbens · 1999
Earlier work this paper cites.
Regression estimation for survey samples
W. A. Fuller · 2002
Earlier work this paper cites.
Boosting with the l 2 loss: regression and classification
P. Bühlmann and B. Yu · 2003
Earlier work this paper cites.
Efficient estimation of average treatment effects using the estimated propensity score
K. Hirano, G. W. Imbens, and G. Ridder · 2003
Earlier work this paper cites.
Higher order properties of gmm and generalized empirical likelihood estimators
W. K. Newey and R. J. Smith · 2004
Earlier work this paper cites.
Twicing kernels and a small bias property of semiparametric estimators
W. K. Newey, F. Hsieh, and J. M. Robins · 2004
Earlier work this paper cites.
Targeted maximum likelihood learning
M. J. Van Der Laan and D. Rubin · 2006
Earlier work this paper cites.
On regularization algorithms in learning theory
F. Bauer, S. Pereverzev, and L. Rosasco · 2007
Earlier work this paper cites.
Optimal rates for the regularized least-squares algorithm
A. Caponnetto and E. De Vito · 2007
Earlier work this paper cites.
Empirical-likelihood-based inference in missing response problems and its application in observational studies
J. Qin and B. Zhang · 2007
Earlier work this paper cites.
Comment: Performance of double-robust estimators when” inverse probability” weights are highly variable
J. Robins, M. Sued, Q. Lei-Gomez, and A. Rotnitzky · 2007
Earlier work this paper cites.
Higher order influence functions and minimax estimation of nonlinear functionals
J. Robins, L. Li, E. Tchetgen, A. van der Vaart, et al · 2008
Earlier work this paper cites.
The elements of statistical learning: data mining, inference, and prediction , volume 2
T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman · 2009
Earlier work this paper cites.
Bayesian variable selection using an adaptive powered correlation prior
A. Krishna, H. D. Bondell, and S. K. Ghosh · 2009
Earlier work this paper cites.
l _ 2 l\_2 boosting in kernel regression
B. Park, Y. Lee, and S. Ha · 2009
Earlier work this paper cites.
Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program
A. Abadie, A. Diamond, and J. Hainmueller · 2010
Earlier work this paper cites.
The horseshoe estimator for sparse signals
C. M. Carvalho, N. G. Polson, and J. G. Scott · 2010
Earlier work this paper cites.
Shrink globally, act locally: Sparse bayesian regularization and prediction
N. G. Polson and J. G. Scott · 2010
Earlier work this paper cites.
Generalized beta mixtures of gaussians
A. Armagan, M. Clyde, and D. Dunson · 2011
Earlier work this paper cites.
Bayesian nonparametric modeling for causal inference
J. L. Hill · 2011
Earlier work this paper cites.
Oaxaca-blinder as a reweighting estimator
P. Kline · 2011
Cited alongside, same era.
Connections between survey calibration estimators and semiparametric models for incomplete data
T. Lumley, P. A. Shaw, and J. Y. Dai · 2011
Cited alongside, same era.
Targeted learning: causal inference for observational and experimental data , volume 4
M. J. Van der Laan, S. Rose, et al · 2011
Cited alongside, same era.
Inverse probability tilting for moment condition models with missing data
B. S. Graham, C. C. de Xavier Pinto, and D. Egel · 2012
Cited alongside, same era.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
Cited alongside, same era.
Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies
J. Hainmueller · 2012
Kernel methods for causal functions: Dose, heterogeneous, and incremental response curves
R. Singh, L. Xu, and A. Gretton · 2020
Later among the works it cites.
Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data
Z. Tan · 2020
Later among the works it cites.
An introduction to proximal causal learning
E. J. T. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao · 2020
Later among the works it cites.
Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations
Y. Wang and J. R. Zubizarreta · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Inference on treatment effects after selection among high-dimensional controls
A. Belloni, V. Chernozhukov, and C. Hansen · 2014
Cited alongside, same era.
Covariate balancing propensity score
K. Imai and M. Ratkovic · 2014
Cited alongside, same era.
Robust inference on average treatment effects with possibly more covariates than observations
M. H. Farrell · 2015
Cited alongside, same era.
Bias-reduced doubly robust estimation
K. Vermeulen and S. Vansteelandt · 2015
Cited alongside, same era.
Stable weights that balance covariates for estimation with incomplete outcome data
J. R. Zubizarreta · 2015
Cited alongside, same era.
The highly adaptive lasso estimator
D. Benkeser and M. Van Der Laan · 2016
Cited alongside, same era.
A. Agarwal and R. Singh · 2021
Later among the works it cites.
Synthetic difference-in-differences
D. Arkhangelsky, S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wager · 2021
Later among the works it cites.
On the implied weights of linear regression for causal inference
A. Chattopadhyay and J. R. Zubizarreta · 2021
Later among the works it cites.
Statistics of robust optimization: A generalized empirical likelihood approach
J. C. Duchi, P. W. Glynn, and H. Namkoong · 2021
Later among the works it cites.
Augmented minimax linear estimation
D. A. Hirshberg and S. Wager · 2021
Later among the works it cites.
Causal inference under unmeasured confounding with negative controls: A minimax learning approach
N. Kallus, X. Mao, and M. Uehara · 2021
Later among the works it cites.
Characterization of parameters with a mixed bias property
A. Rotnitzky, E. Smucler, and J. M. Robins · 2021
Later among the works it cites.
M. Rubinstein, A. Haviland, and D. Choi · 2021
Later among the works it cites.
R. Singh · 2021
Later among the works it cites.
Hierarchical shrinkage: Improving the accuracy and interpretability of tree-based models
A. Agarwal, Y. S. Tan, O. Ronen, C. Singh, and B. Yu · 2022
Later among the works it cites.
Outcome assumptions and duality theory for balancing weights
D. A. Bruns-Smith and A. Feller · 2022
Later among the works it cites.
Soft calibration for selection bias problems under mixed-effects models
C. Gao, S. Yang, and J. K. Kim · 2022
Later among the works it cites.
Minimax kernel machine learning for a class of doubly robust functionals with application to proximal causal inference
A. Ghassami, A. Ying, I. Shpitser, and E. T. Tchetgen · 2022
Later among the works it cites.
Surprises in high-dimensional ridgeless least squares interpolation
T. Hastie, A. Montanari, S. Rosset, and R. J. Tibshirani · 2022
Later among the works it cites.
Semiparametric doubly robust targeted double machine learning: a review
E. H. Kennedy · 2022
Later among the works it cites.
On reproducing kernel banach spaces: Generic definitions and unified framework of constructions
R. R. Lin, H. Z. Zhang, and J. Zhang · 2022
Later among the works it cites.
On regression-adjusted imputation estimators of the average treatment effect
Z. Lin and F. Han · 2022
Later among the works it cites.
A tale of two panel data regressions
D. Shen, P. Ding, J. Sekhon, and B. Yu · 2022
Later among the works it cites.
Double robustness for complier parameters and a semiparametric test for complier characteristics
R. Singh, L. Sun, et al · 2022
Later among the works it cites.
The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters
N. Moosavi, J. Häggström, and X. de Luna · 2023
Closest in time.
W. Mou, P. Ding, M. J. Wainwright, and P. L. Bartlett · 2023
Closest in time.
Ultra-high dimensional variable selection for doubly robust causal inference
D. Tang, D. Kong, W. Pan, and L. Wang · 2023
Closest in time.
The role of simplex constraints in regularizing treatment effect estimates
D. Arbour and A. Feller · 2024
Closest in time.
Hyperparameter tuning for causal inference with double machine learning: A simulation study
P. Bach, O. Schacht, V. Chernozhukov, S. Klaassen, and M. Spindler · 2024
Closest in time.
Automatic debiased machine learning via riesz regression, 2024
V. Chernozhukov, W. K. Newey, V. Quintas-Martinez, and V. Syrgkanis · 2024
Closest in time.
Bayesian causal models from a weighting perspective: Balance, bias, and double robustness
J. Murray and A. Feller · 2024
Closest in time.