Fetching the paper…
Reading the bibliography…
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation.
Sur les applications de la théorie des probabilités aux experiences agricoles: Essai des principes
J. Neyman · 1923
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
D. B. Rubin · 1974
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.
On asymptotically efficient estimation in semiparametric models
A. Schick · 1986
Earlier work this paper cites.
Root-n-consistent semiparametric regression
P. M. Robinson · 1988
Earlier work this paper cites.
Multivariate adaptive regression splines
J. H. Friedman · 1991
Earlier work this paper cites.
Stacked generalization
D. H. Wolpert · 1992
Earlier work this paper cites.
Efficient and adaptive estimation for semiparametric models
P. J. Bickel, C. A. Klaassen, P. J. Bickel, Y. Ritov, J. Klaassen, J. A. Wellner, and Y. Ritov · 1993
Earlier work this paper cites.
The asymptotic variance of semiparametric estimators
W. K. Newey · 1994
Earlier work this paper cites.
Sharper bounds for Gaussian and empirical processes
M. Talagrand · 1994
Earlier work this paper cites.
Semiparametric efficiency in multivariate regression models with missing data
J. M. Robins and A. Rotnitzky · 1995
Earlier work this paper cites.
Stacked regressions
L. Breiman · 1996
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
Earlier work this paper cites.
Adjusting for nonignorable drop-out using semiparametric nonresponse models
D. O. Scharfstein, A. Rotnitzky, and J. M. Robins · 1999
Earlier work this paper cites.
About the constants in Talagrand’s concentration inequalities for empirical processes
P. Massart · 2000
Earlier work this paper cites.
On the mathematical foundations of learning
F. Cucker and S. Smale · 2002
Earlier work this paper cites.
Estimating the approximation error in learning theory
S. Smale and D.-X. Zhou · 2003
Earlier work this paper cites.
Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: Finite sample oracle inequalities and examples
M. J. van der Laan and S. Dudoit · 2003
Earlier work this paper cites.
Optimal structural nested models for optimal sequential decisions
J. M. Robins · 2004
Earlier work this paper cites.
Comparing experimental and matching methods using a large-scale voter mobilization experiment
K. Arceneaux, A. S. Gerber, and D. P. Green · 2006
Earlier work this paper cites.
Empirical minimization
P. L. Bartlett and S. Mendelson · 2006
Earlier work this paper cites.
The generic chaining: Upper and lower bounds of stochastic processes
M. Talagrand · 2006
Earlier work this paper cites.
Targeted maximum likelihood learning
M. J. van der Laan and D. Rubin · 2006
Earlier work this paper cites.
The cross-validated adaptive epsilon-net estimator
M. J. van der Laan, S. Dudoit, and A. W. van der Vaart · 2006
Cited alongside, same era.
Optimal rates for the regularized least-squares algorithm
A. Caponnetto and E. De Vito · 2007
Cited alongside, same era.
Semiparametric theory and missing data
A. Tsiatis · 2007
Cited alongside, same era.
Super learner
M. J. van der Laan, E. C. Polley, and A. E. Hubbard · 2007
Cited alongside, same era.
Consistency of cross validation for comparing regression procedures
Y. Yang · 2007
Cited alongside, same era.
Fast rates for estimation error and oracle inequalities for model selection
P. L. Bartlett · 2008
Cited alongside, same era.
Support Vector Machines
Predicting the future—big data, machine learning, and clinical medicine
Z. Obermeyer and E. J. Emanuel · 2016
Later among the works it cites.
High-dimensional regression adjustments in randomized experiments
S. Wager, W. Du, J. Taylor, and R. J. Tibshirani · 2016
Later among the works it cites.
Beyond prediction: Using big data for policy problems
S. Athey · 2017
Closest in time.
S. Athey and S. Wager · 2017
Closest in time.
Program evaluation and causal inference with high-dimensional data
A. Belloni, V. Chernozhukov, I. Fernández-Val, and C. Hansen · 2017
Closest in time.
Kernel-based regularized least squares in R (KRLS) and Stata (krls)
J. Ferwerda, J. Hainmueller, and C. J. Hazlett · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
I. Steinwart and A. Christmann · 2008
Cited alongside, same era.
Asymptotics for statistical treatment rules
K. Hirano and J. R. Porter · 2009
Cited alongside, same era.
Optimal rates for regularized least squares regression
I. Steinwart, D. R. Hush, and C. Scovel · 2009
Cited alongside, same era.
Subgroup analysis via recursive partitioning
X. Su, C.-L. Tsai, H. Wang, D. M. Nickerson, and B. Li · 2009
Cited alongside, same era.
Regularization paths for generalized linear models via coordinate descent
J. Friedman, T. Hastie, and R. Tibshirani · 2010
Cited alongside, same era.
Regularization in kernel learning
S. Mendelson and J. Neeman · 2010
Cited alongside, same era.
Closest in time.
Minimax estimation of a functional on a structured high-dimensional model
J. M. Robins, L. Li, R. Mukherjee, E. Tchetgen Tchetgen, and A. van der Vaart · 2017
Closest in time.
Estimating individual treatment effect: generalization bounds and algorithms
U. Shalit, F. D. Johansson, and D. Sontag · 2017
Closest in time.
Selective inference for effect modification via the lasso
Q. Zhao, D. S. Small, and A. Ertefaie · 2017
Closest in time.
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
Closest in time.
Who should be treated? empirical welfare maximization methods for treatment choice
T. Kitagawa and A. Tetenov · 2018
Closest in time.
Some methods for heterogeneous treatment effect estimation in high dimensions
S. Powers, J. Qian, K. Jung, A. Schuler, N. H. Shah, T. Hastie, and R. Tibshirani · 2018
Closest in time.
Estimation and inference of heterogeneous treatment effects using random forests
S. Wager and S. Athey · 2018
Closest in time.
Generalized random forests
S. Athey, J. Tibshirani, and S. Wager · 2019
Closest in time.
Decomposing treatment effect variation
P. Ding, A. Feller, and L. Miratrix · 2019
Closest in time.
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
V. Dorie, J. Hill, U. Shalit, M. Scott, and D. Cervone · 2019
Closest in time.
Orthogonal statistical learning
D. J. Foster and V. Syrgkanis · 2019
Closest in time.
Metalearners for estimating heterogeneous treatment effects using machine learning
S. R. Künzel, J. S. Sekhon, P. J. Bickel, and B. Yu · 2019
Closest in time.
R: A Language and Environment for Statistical Computing
R Core Team · 2019
Closest in time.
Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
P. R. Hahn, J. S. Murray, and C. M. Carvalho · 2020
Closest in time.
Optimal doubly robust estimation of heterogeneous causal effects
E. H. Kennedy · 2020
Closest in time.
Cross-validation, risk estimation, and model selection: Comment on a paper by rosset and tibshirani
S. Wager · 2020
Closest in time.