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Causal inference with observational studies often relies on the assumptions of unconfoundedness and overlap of covariate distributions in different treatment groups.
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The Jackknife and Bootstrap
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Li, F., Morgan, K. L. and Zaslavsky, A. M. (2016) · 2016
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Dealing with limited overlap in estimation of average treatment effects, Biometrika
Crump, R. K., Hotz, V. J., Imbens, G. W. and Mitnik, O. A. (2009) · 2009
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Improving propensity score weighting using machine learning, Stat Med
Lee, B. K., Lessler, J. and Stuart, E. A. (2010) · 2010
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Irregular identification, support conditions, and inverse weight estimation, Econometrica
Khan, S. and Tamer, E. (2010) · 2042
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