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@article{Rosenbaum2012,
	Author = {Paul R. Rosenbaum},
	Date-Added = {2013-05-18 14:21:18 +0000},
	Date-Modified = {2013-05-18 14:22:21 +0000},
	Journal = {Biometrika},
	Pages = {1--12},
	Title = {Testing one hypothesis twice in observational studies},
	Year = {2012}}

@article{Austin2010,
	Author = {Peter Austin},
	Date-Added = {2013-05-18 13:14:18 +0000},
	Date-Modified = {2013-05-18 13:15:46 +0000},
	Journal = {Statist. Med.},
	Pages = {1292--1301},
	Title = {Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples},
	Volume = {10},
	Year = {2010}}

@article{Austin2008,
	Author = {Peter Austin},
	Journal = {Statistics in Medicine},
	Owner = {jbryer},
	Pages = {2037--2049},
	Timestamp = {2013.03.07},
	Title = {A Critical Appraisal of Propensity-Score Matching in the Medical Literature Between 1996 and 2003},
	Volume = {27},
	Year = {2008}}

@article{ImaiVanDyk2004,
	Abstract = {In this article we develop the theoretical properties of the propensity
	function, which is a generalization of the propensity score of
	
	Rosenbaum and Rubin. Methods based on the propensity score have long
	been used for causal inference in observational studies; they are
	easy to use and can effectively reduce the bias caused by nonrandom
	treatment assignment. Although treatment regimes need not be binary
	in practice, the propensity score methods are generally confined to
	binary treatment scenarios. Two possible exceptions have been suggested
	for ordinal and categorical treatments. In this article we develop
	theory and methods that encompass all of these techniques and widen
	their applicability by allowing for arbitrary treatment regimes.
	We illustrate our propensity function methods by applying them to
	two datasets; we estimate the effect of smoking on medical expenditure
	and the effect of schooling on wages. We also conduct simulation
	studies to investigate the performance of our methods.},
	Author = {Kosuke Imai and David A. van Dyk},
	Journal = {Journal of the American Statistical Association},
	Number = {467},
	Pages = {854--866},
	Title = {Causal Inference with General Treatment Regimes: Generalizing the Propensity Score},
	Url = {http://imai.princeton.edu/research/pscore.html},
	Volume = {99},
	Year = {2004},
	Bdsk-Url-1 = {http://imai.princeton.edu/research/pscore.html}}

@article{RosenbaumRubin1983,
	Abstract = {The propensity score is the conditional probability of assignment
	to a particular treatment given a vector of observed covariates.
	Both large and small sample theory show that adjustment for the scalar
	propensity score is sufficient to remove bias due to all observed
	covariates. Applications include: (i) matched sampling on the univariate
	propensity score, which is a generalization of discriminant matching,
	(ii) multivariate adjustment by subclassification on the propensity
	score where the same subclasses are used to estimate treatment effects
	for all outcome variables and in all subpopulations, and (iii) visual
	representation of multivariate covariance adjustment by a two-dimensional
	plot.},
	Author = {Paul R. Rosenbaum and Donald B. Rubin},
	Journal = {Biometrika},
	Owner = {jbryer},
	Pages = {41--55},
	Timestamp = {2013.03.07},
	Title = {The Central Role of the Propensity Score in Observational Studies for Causal Effects},
	Volume = {70},
	Year = {1983}}

@article{RosenbaumRubin1985,
	Author = {Paul R. Rosenbaum and Donald B. Rubin},
	Issue = {1},
	Journal = {The American Statistician},
	Title = {Constructing a control group using multivariate matched sampling methods that incorporate the propensity score},
	Volume = {39},
	Year = {1985}}

@article{ThoemmesKim2011,
	Abstract = {The use of propensity scores in psychological and educational research
	has been steadily increasing in the last 2 to 3 years. However, there
	are some common misconceptions about the use of different estimation
	techniques and conditioning choices in the context of propensity
	score analysis. In addition, reporting practices for propensity score
	analyses often lack important details that allow other researchers
	to confidently judge the appropriatenessof reported analyses and
	potentially to replicate published findings. In this article we conduct
	a systematic literature review of a large number of published articles
	in major areas of social science that used propensity scores up until
	the fall of 2009. We identify common errors in estimation, conditioning,
	and reporting of propensity score analyses and suggest possible solutions.},
	Author = {Felix J. Thoemmes and Eun Sook Kim},
	Journal = {Multivariate Behavioral Research},
	Owner = {jbryer},
	Pages = {90--118},
	Timestamp = {2013.03.07},
	Title = {A Systematic Review of Propensity Score Methods in the Social Sciences},
	Volume = {46},
	Year = {2011}}

@electronic{Zhaoetal2012,
	Abstract = {Propensity score methods have become a part of the standard toolkit
	for applied researchers who wish to ascertain causal effects from
	observational data. While they were originally developed for binary
	treatments, several researchers have proposed generalizations of
	the propensity score methodology for non-binary treatment regimes.
	Such extensions have widened the applicability of propensity score
	methods and are indeed becoming increasingly popular themselves.
	In this article, we closely examine the two main generalizations
	of propensity score methods, namely, the generalized propensity score
	(gps) of Hirano and Imbens (2004) and the propensity function (p-function)
	of Imai and van Dyk (2004). We compare the assumptions, theoretical
	properties, and empirical performance of these two alternative methodologies.
	On a theoretical level, the gps is advantageous in that it can be
	used to estimate the full dose response function rather than the
	simple average treatment effect that is typically estimated with
	the p-function. Unfortunately, our analysis shows that in practice
	the response model used with the GPS is less exible than those typically
	used with propensity score methods and is prone to misspecification.
	We propose methods to improve the robustness of the gps to potential
	model misspecification and the exibility of the p-function in estimating
	the dose response function. We illustrate our findings and proposals
	through simulation studies, including one based on an empirical application.},
	Author = {Shandong Zhao and David A. van Dyk and Kosuke},
	Month = {November},
	Owner = {jbryer},
	Timestamp = {2013.03.07},
	Title = {Causal Inference in Observational Studies with Non-Binary Treatments},
	Year = {2012}}

@article{HelmreichPruzek2008,
	Accepted = {2008-12-22},
	Author = {James E. Helmreich and Robert M. Pruzek},
	Bibdate = {2008-12-22},
	Coden = {JSSOBK},
	Day = {20},
	Issn = {1548-7660},
	Journal = {Journal of Statistical Software},
	Month = {2},
	Number = {6},
	Pages = {1--23},
	Submitted = {2008-09-24},
	Title = {PSAgraphics: An R Package to Support Propensity Score Analysis},
	Url = {https://www.jstatsoft.org/v29/i06},
	Volume = {29},
	Year = {2009},
	Bdsk-Url-1 = {https://www.jstatsoft.org/v29/i06}}

@article{RosenbaumRubin1984,
	Author = {Paul R. Rosenbaum and Donald B. Rubin},
	Journal = {Journal of the American Statistical Association},
	Pages = {516---524},
	Title = {Reducing bias in observational studies using subclassification on the propensity score},
	Volume = {79},
	Year = {1984}}

@manual{rcore,
	Address = {Vienna, Austria},
	Author = {{R Development Core Team}},
	Note = {{ISBN} 3-900051-07-0},
	Organization = {R Foundation for Statistical Computing},
	Title = {R: A Language and Environment for Statistical Computing},
	Url = {http://www.R-project.org/},
	Year = {2011},
	Bdsk-Url-1 = {http://www.R-project.org/}}

@book{ggplot2,
	Author = {Hadley Wickham},
	Isbn = {978-0-387-98140-6},
	Publisher = {Springer New York},
	Title = {ggplot2: elegant graphics for data analysis},
	Url = {http://had.co.nz/ggplot2/book},
	Year = {2009},
	Bdsk-Url-1 = {http://had.co.nz/ggplot2/book}}

@manual{devtools,
	Author = {Hadley Wickham},
	Note = {R package version 0.6},
	Title = {devtools: Tools to make developing R code easier},
	Url = {http://CRAN.R-project.org/package=devtools},
	Year = {2012},
	Bdsk-Url-1 = {http://CRAN.R-project.org/package=devtools}}

@article{Johnson2003,
	Author = {Elizabeth Johnson and Francesca Dominici and Michael Griswold and Scott L. Zeger},
	Journal = {Journal of Econometrics},
	Pages = {135--151},
	Title = {Disease Cases and Their Medical Costs Attributable to Smoking: An Analysis of the National Medical Expenditure Survey},
	Volume = {112},
	Year = {2003}}

@misc{nmes,
	Author = {{National Center For Health Services Research}},
	Title = {National Medical Expenditure Survey. Methods II. Questionnaires and data collection methods for the household survey and the Survey of American Indians and Alaska Natives. National Center for Health Services Research and Health Technology Assessment.},
	Year = {1987}}
