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Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap.
Eine informationstheoretische Ungleichung und ihre anwendung auf den Beweis der ergodizität von Markoffschen Ketten
Csiszár, I · 1963
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A General Class of Coefficients of Divergence of One Distribution from Another
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χ α \chi^{\alpha} -divergence and generalized Fisher’s information
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The central role of the propensity score in observational studies for causal effects
Rosenbaum, P · 1983
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Information-type divergence when the likelihood ratios are bounded
Rukhin, A. L · 1997
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Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
Dehejia, R. H · 1999
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Subsampling inference for the mean in the heavy-tailed case
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Rosenbaum, P. R · 2002
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Efficient estimation of average treatment effects using the estimated propensity score
Hirano, K · 2003
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Some theory for Fisher’s linear discriminant function, ‘naive Bayes’, and some alternatives when there are many more variables than observations
Bickel, P. J · 2004
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Nonparametric estimation of average treatment effects under exogeneity: A review
Imbens, G. W · 2004
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Entropy, Relative Entropy, and Mutual Information
Cover, T. M · 2005
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Moving the goalposts: Addressing limited overlap in the estimation of average treatment effects by changing the estimand
Crump, R · 2006
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On divergences and informations in statistics and information theory
Liese, F · 2006
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Semiparametric efficiency in gmm models with auxiliary data
Chen, X · 2008
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The prognostic analogue of the propensity score
Hansen, B. B · 2008
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Dealing with limited overlap in estimation of average treatment effects
Crump, R. K · 2009
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Should observational studies be designed to allow lack of balance in covariate distributions across treatment groups?
Rubin, D. B · 2009
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On a Class of Bias-Amplifying Variables that Endanger Effect Estimates
Pearl, J · 2010
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Collaborative Double Robust Targeted Maximum Likelihood Estimation
van der Laan, M. J · 2010
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Chen, X · 2011
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Robust inference on average treatment effects with possibly more covariates than observations
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Should instrumental variables be used as matching variables?
Wooldridge, J. M · 2016
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Instrumental variables as bias amplifiers with general outcome and confounding
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Augmented minimax linear estimation
Hirshberg, D. A · 2017
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Luo, W · 2017
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Robust confidence intervals for average treatment effects under limited overlap
Rothe, C · 2017
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