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Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available.
Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome
Rosenbaum, P. R. and Rubin, D. B · 1983
Earlier work this paper cites.
Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties
MacKinnon, J. G. and White, H · 1985
Earlier work this paper cites.
Root-n-consistent semiparametric regression
Robinson, P. M · 1988
Earlier work this paper cites.
Estimation of regression coefficients when some regressors are not always observed
Robins, J. M., Rotnitzky, A., and Zhao, L. P · 1994
Earlier work this paper cites.
Semiparametric efficiency in multivariate regression models with missing data
Robins, J. M. and Rotnitzky, A · 1995
Earlier work this paper cites.
The effectiveness of right heart catheterization in the initial care of critically ill patients
Connors, A. F., Speroff, T., Dawson, N. V., Thomas, C., Harrell, F. E., Wagner, D., Desbiens, N., Goldman, L., Wu, A. W., Califf, R. M., and Fulkerson, W. J · 1996
Earlier work this paper cites.
Regression shrinkage and selection via the Lasso
Tibshirani, R · 1996
Earlier work this paper cites.
Semiparametric regression for repeated outcomes with nonignorable nonresponse
Rotnitzky, A., Robins, J. M., and Scharfstein, D. O · 1998
Earlier work this paper cites.
Adjusting for nonignorable drop-out using semiparametric nonresponse models
Scharfstein, D. O., Rotnitzky, A., and Robins, J. M · 1999
Earlier work this paper cites.
Random forests
Breiman, L · 2001
Earlier work this paper cites.
On the mathematical foundations of learning
Cucker, F. and Smale, S · 2002
Earlier work this paper cites.
Empirical minimization
Bartlett, P. L. and Mendelson, S · 2006
Earlier work this paper cites.
Statistical inference for variable importance
van der Laan, M. J · 2006
Earlier work this paper cites.
Support vector machines
Steinwart, I. and Christmann, A · 2008
Earlier work this paper cites.
Causality
Pearl, J · 2009
Earlier work this paper cites.
Regularization paths for generalized linear models via coordinate descent
Friedman, J., Hastie, T., and Tibshirani, R · 2010
Earlier work this paper cites.
Negative controls: a tool for detecting confounding and bias in observational studies
Lipsitch, M., Tchetgen Tchetgen, E., and Cohen, T · 2010
Earlier work this paper cites.
Regularization in kernel learning
Mendelson, S. and Neeman, J · 2010
Earlier work this paper cites.
Bayesian nonparametric modeling for causal inference
Hill, J. L · 2011
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Measurement bias and effect restoration in causal inference
Kuroki, M. and Pearl, J · 2014
Earlier work this paper cites.
Causal inference in statistics, social, and biomedical sciences
Imbens, G. W. and Rubin, D. B · 2015
Earlier work this paper cites.
Recursive partitioning for heterogeneous causal effects
Athey, S. and Imbens, G · 2016
Earlier work this paper cites.
XGBoost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Learning representations for counterfactual inference
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Super-learning of an optimal dynamic treatment rule
Luedtke, A. R. and van der Laan, M. J · 2016
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Bayesian inference of individualized treatment effects using multi-task gaussian processes
Alaa, A. M. and van der Schaar, M · 2017
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Shalit, U., Johansson, F. D., and Sontag, D · 2017
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Later among the works it cites.
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