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Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R
TLDR
Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. Expand
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Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies
This paper presents genetic matching, a method of multivariate matching that uses an evolutionary search algorithm to determine the weight each covariate is given. Both propensity score matching andExpand
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Appendices for Elections and the Regression-Discontinuity Design: Lessons from Close U.S. House Races, 1942-2008
Following David Lee’s pioneering work, numerous scholars have applied the regression discontinuity (RD) design to popular elections. Contrary to the assumptions of RD, however, we show that bareExpand
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Genetic Optimization Using Derivatives: The rgenoud Package for R
TLDR
This introduction to the R package rgenoud is a modied version of Mebane and Sekhon (2011), published in the Journal of Statistical Software. Expand
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Opiates for the Matches: Matching Methods for Causal Inference
In recent years, there has been a burst of innovative work on methods for estimating causal effects using observational data. Much of this work has extended and brought a renewed focus on oldExpand
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The Butterfly Did It: The Aberrant Vote for Buchanan in Palm Beach County, Florida
We show that the butterfly ballot used in Palm Beach County, Florida, in the 2000 presidential election caused more than 2,000 Democratic voters to vote by mistake for Reform candidate Pat Buchanan,Expand
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The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods
Wand for many valuable discussions on these topics. All errors are my responsibility.
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When Natural Experiments Are Neither Natural nor Experiments
TLDR
Natural experiments help to overcome some of the obstacles researchers face when making causal inferences in the social sciences. Expand
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Endogeneity in Probit Response Models
We look at conventional methods for removing endogeneity bias in regression models, including the linear model and the probit model. It is known that the usual Heckman two-step procedure should notExpand
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The Relative Performance of Targeted Maximum Likelihood Estimators
There is an active debate in the literature on censored data about the relative performance of model based maximum likelihood estimators, IPCW-estimators, and a variety of double robustExpand
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