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Bibliography
Abadie, A. and Imbens, G. W. (2007), ``Bias-Corrected Matching Estimators for Average Treatment Effects,'' http://ksghome.harvard.edu/ aabadie/research.html.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984),
Classification and Regression Trees
, New York, New York: Chapman & Hall.
Dehejia, R. H. and Wahba, S. (1999), ``Causal Effects in Nonexperimental Studies: Re-Evaluating the Evaluation of Training Programs,''
Journal of the American Statistical Association
, 94, 1053-62.
Diamond, A. and Sekhon, J. (2005), ``Genetic Matching for Estimating Causal Effects: A New Method of Achieving Balance in Observational Studies,'' http://jsekhon.fas.harvard.edu/.
Gu, X. and Rosenbaum, P. R. (1993), ``Comparison of multivariate matching methods: structures, distances, and algorithms,''
Journal of Computational and Graphical Statistics
, 2, 405-420.
Hansen, B. B. (2004), ``Full Matching in an Observational Study of Coaching for the SAT,''
Journal of the American Statistical Association
, 99, 609-618.
Hastie, T. J. and Tibshirani, R. (1990),
Generalized Additive Models
, London: Chapman Hall.
Ho, D., Imai, K., King, G., and Stuart, E. (2007), ``Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference,''
Political Analysis
, 15, 199-236, http://gking.harvard.edu/files/abs/matchp-abs.shtml.
Ho, D. E., Imai, K., King, G., and Stuart, E. A. (Forthcoming), ``MatchIt: Nonparametric Preprocessing for Parametric Causal Inference,''
Journal of Statistical Software
, http://gking.harvard.edu/matchit.
Imai, K. (2005), ``Do Get-Out-The-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments,''
American Political Science Review
, 99, 283-300.
Imai, K., King, G., and Lau, O. (2006), ``Zelig: Everyone's Statistical Software,'' http://gking.harvard.edu/zelig.
Imai, K., King, G., and Stuart, E. (2008), ``Misunderstandings Among Experimentalists and Observationalists about Causal Inference,''
Journal of the Royal Statistical Society, Series A
, 171, part 2, 481-502, http://gking.harvard.edu/files/abs/matchse-abs.shtml.
Imai, K. and van Dyk, D. A. (2004), ``Causal Inference with General Treatment Treatment Regimes: Generalizing the Propensity Score,''
Journal of the American Statistical Association
, 99, 854-866.
King, G., Honaker, J., Joseph, A., and Scheve, K. (2001), ``Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation,''
American Political Science Review
, 95, 49-69, http://gking.harvard.edu/files/abs/evil-abs.shtml.
King, G. and Zeng, L. (2006), ``The Dangers of Extreme Counterfactuals,''
Political Analysis
, 14, 131-159, http://gking.harvard.edu/files/abs/counterft-abs.shtml.
-- (2007), ``When Can History Be Our Guide? The Pitfalls of Counterfactual Inference,''
International Studies Quarterly
, 183-210, http://gking.harvard.edu/files/abs/counterf-abs.shtml.
Lalonde, R. (1986), ``Evaluating the Econometric Evaluations of Training Programs,''
American Economic Review
, 76, 604-620.
Ripley, B. (1996),
Pattern Recognition and Neural Networks
, Cambridge Univeristy Press.
Rosenbaum, P. R. (2002),
Observational Studies, 2nd Edition
, New York, NY: Springer Verlag.
Stoll, H., King, G., and Zeng, L. (2005), ``WhatIf: Software for Evaluating Counterfactuals,''
Journal of Statistical Software
, 15, http://www.jstatsoft.org/index.php?vol=15.
Venables, W. N. and Ripley, B. D. (2002),
Modern Applied Statistics with S
, Springer-Verlag, 4th ed.
Gary King 2010-12-11