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Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
A unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply the best parametric techniques they would have used anyway. Expand
Logistic Regression in Rare Events Data
We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). Expand
Causal Inference without Balance Checking: Coarsened Exact Matching
We discuss a method for improving causal inferences called “Coarsened Exact Matching” (CEM), and the new “Monotonic Imbalance Bounding” (MIB) class of matching methods from which CEM is derived. WeExpand
Designing Social Inquiry: Scientific Inference in Qualitative Research.
While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba haveExpand
Amelia II: A Program for Missing Data
Amelia II is a complete R package for multiple imputation of missing data. Expand
Enhancing the Validity and Cross-Cultural Comparability of Measurement in Survey Research
We address two long-standing survey research problems: measuring complicated concepts, such as political freedom and efficacy, that researchers define best with reference to examples; and what to doExpand
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions by preprocessing data with nonparametric and semi-parametric matching methods. Expand
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticiansExpand
Making the Most Of Statistical Analyses: Improving Interpretation and Presentation
We offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner. Expand
Cem: Coarsened Exact Matching in Stata
In this article, we introduce a Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated andExpand