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Many decisions in healthcare, business, and other policy domains are made without the support of rigorous evidence due to the cost and complexity of performing randomized experiments.
The Gradient Projection Method for Nonlinear Programming. Part II. Nonlinear Constraints
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The central role of the propensity score in observational studies for causal effects
Donald B Rubin and Paul R Rosenbaum · 1983
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Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
James M Robins, Steven D Mark, and Whitney K Newey · 1992
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An Introduction to the Bootstrap
Bradley Efron and Robert J Tibshirani · 1993
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Greedy function approximation: a gradient boosting machine
J H Friedman · 2001
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The Dangers of Extreme Counterfactuals
G King · 2005
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Randomized Clinical Trials and Observational Studies
Edward L Hannan · 2008
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Evaluating uses of data mining techniques in propensity score estimation: a simulation study
Soko Setoguchi, Sebastian Schneeweiss, M Alan Brookhart, Robert J Glynn, and E Francis Cook · 2008
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The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Improving propensity score weighting using machine learning
Brian K Lee, Elizabeth A Stuart, and Justin Lessler · 2009
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On the limitations of comparative effectiveness research
Donald B Rubin · 2010
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To Explain or to Predict?
Galit Shmueli · 2010
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Matching Methods for Causal Inference: A Review and a Look Forward
Elizabeth A Stuart · 2010
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Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies
Peter C Austin · 2011
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Bayesian Nonparametric Modeling for Causal Inference
Jennifer L Hill · 2011
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Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases
Jessica M Franklin, Jeremy A Rassen, Sebastian Schneeweiss, and Jennifer M Polinski · 2014
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A Systematic Statistical Approach to Evaluating Evidence from Observational Studies
David Madigan, Paul E Stang, Jesse A Berlin, Martijn Schuemie, J Marc Overhage, Marc A Suchard, Bill Dumouchel, Abraham G Hartzema, and Patrick B Ryan · 2014
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Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs
T Sturmer, R Wyss, R J Glynn, and M A Brookhart · 2014
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A Theory of Statistical Inference for Matching Methods in Applied Causal Research
S M Iacus, G King, and G Porro · 2015
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Double robust matching estimators for high dimensional confounding adjustment
Joseph Antonelli, Matthew Cefalu, Nathan Palmer, and Denis Agniel · 2016
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Using Ensemble-Based Methods for Directly Estimating Causal Effects: An Investigation of Tree-Based G-Computation
Peter C Austin · 2012
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On the joint use of propensity and prognostic scores in estimation of the average treatment effect on the treated: a simulation study
Finbarr P Leacy and Elizabeth A Stuart · 2013
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Estimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions
Elizabeth A Stuart, Eva DuGoff, Michael Abrams, David Salkever, and Donald Steinwachs · 2013
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Optimizing matching and analysis combinations for estimating causal effects
K Ellicott Colson, Kara E Rudolph, Scott C Zimmerman, Dana E Goin, Elizabeth A Stuart, Mark van der Laan, and Jennifer Ahern · 2016
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Some methods for heterogeneous treatment effect estimation in high-dimensions
Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H Shah, Trevor Hastie, and Robert Tibshirani · 2017
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Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies
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