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Weighting methods are widely used to adjust for covariates in observational studies, sample surveys, and regression settings.
On calculating with b-splines
De Boor, C. (1972) · 1972
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Matching to remove bias in observational studies
Rubin, D. B. (1973) · 1973
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The central role of the propensity score in observational studies for causal effects
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Model-based direct adjustment
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Tseng, P. and Bertsekas, D. P. (1987) · 1987
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Rosenbaum, P. R. (1989) · 1989
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Tseng, P. and Bertsekas, D. P. (1991) · 1991
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Deville, J.-C. and Särndal, C.-E. (1992) · 1992
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Weak convergence
Van Der Vaart, A. W. and Wellner, J. A. (1996) · 1996
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Convergence rates and asymptotic normality for series estimators
Newey, W. K. (1997) · 1997
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Non-response models for the analysis of non-monotone ignorable missing data
Robins, J. M. and Gill, R. D. (1997) · 1997
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On the role of the propensity score in efficient semiparametric estimation of average treatment effects
Hahn, J. (1998) · 1998
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Imposing moment restrictions from auxiliary data by weighting
Hellerstein, J. K. and Imbens, G. W. (1999) · 1999
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Efficient estimation of average treatment effects using the estimated propensity score
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Boyd, S. and Vandenberghe, L. (2004) · 2004
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Full matching in an observational study of coaching for the SAT
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Nonparametric estimation of an additive model with a link function
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Large sample properties of matching estimators for average treatment effects
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Imai, K. and Ratkovic, M. (2014) · 2014
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Statistical analysis with missing data
Little, R. J. and Rubin, D. B. (2014) · 2014
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Some new asymptotic theory for least squares series: Pointwise and uniform results
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Stable weights that balance covariates for estimation with incomplete outcome data
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Chan, K. C. G., Yam, S. C. P., and Zhang, Z. (2016) · 2016
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Comment: Performance of double-robust estimators when" inverse probability" weights are highly variable
Robins, J., Sued, M., Lei-Gomez, Q., and Rotnitzky, A. (2007) · 2007
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For objective causal inference, design trumps analysis
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