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Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction.
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. 1974 · 1974
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
Rosenbaum, P. R.; and Rubin, D. B. 1983 · 1983
Earlier work this paper cites.
Mixture density networks
Bishop, C. M. 1994 · 1994
Earlier work this paper cites.
Least squares support vector machine classifiers
Suykens, J. A.; and Vandewalle, J. 1999 · 1999
Earlier work this paper cites.
Random forests
Breiman, L. 2001 · 2001
Earlier work this paper cites.
The adaptive lasso and its oracle properties
Zou, H. 2006 · 2006
Earlier work this paper cites.
Invited commentary: variable selection versus shrinkage in the control of multiple confounders
Greenland, S. 2008 · 2008
Earlier work this paper cites.
A tutorial on learning with Bayesian networks
Heckerman, D. 2008 · 2008
Earlier work this paper cites.
Exploring strategies for training deep neural networks
Larochelle, H.; Bengio, Y.; Louradour, J.; and Lamblin, P. 2009 · 2009
Earlier work this paper cites.
Overadjustment bias and unnecessary adjustment in epidemiologic studies
Schisterman, E. F.; Cole, S. R.; and Platt, R. W. 2009 · 2009
Earlier work this paper cites.
Effects of adjusting for instrumental variables on bias and precision of effect estimates
Myers, J. A.; Rassen, J. A.; Gagne, J. J.; Huybrechts, K. F.; Schneeweiss, S.; Rothman, K. J.; Joffe, M. M.; and Glynn, R. J. 2011 · 2011
Earlier work this paper cites.
A review of covariate selection for non-experimental comparative effectiveness research
Sauer, B. C.; Brookhart, M. A.; Roy, J.; and VanderWeele, T. 2013 · 2013
Earlier work this paper cites.
Confounder selection via penalized credible regions
Wilson, A.; and Reich, B. J. 2014 · 2014
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Regularization methods for high-dimensional instrumental variables regression with an application to genetical genomics
Lin, W.; Feng, R.; and Li, H. 2015 · 2015
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Chen, T.; and Guestrin, C. 2016 · 2016
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
Learning infomax and domain-independent representations for causal effect inference with real-world data
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Later among the works it cites.
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Later among the works it cites.
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