MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

June 2011, Volume 42, Issue 8 | Daniel E. Ho, Kosuke Imai, Gary King, Elizabeth A. Stuart
MatchIt is a software package implemented in R that implements nonparametric matching methods to improve parametric statistical models. It reduces the dependence of causal inferences on hard-to-justify statistical modeling assumptions by preprocessing data with sophisticated matching techniques. MatchIt supports a wide range of matching methods, including exact, subclassification, nearest neighbor, optimal, and genetic matching. The software is designed for causal inference with a dichotomous treatment variable and pretreatment control variables, and can be used for both experimental and observational studies. MatchIt provides tools to assess balance, which is crucial for ensuring that the matching procedure effectively reduces confounding variables. After preprocessing, researchers can use any parametric model they would have used without MatchIt, but with more robust and less sensitive inferences. MatchIt is integrated with Zelig, an R package that simplifies the process of conducting parametric analyses on matched data. The package includes detailed documentation and examples to guide users through the matching and analysis process.MatchIt is a software package implemented in R that implements nonparametric matching methods to improve parametric statistical models. It reduces the dependence of causal inferences on hard-to-justify statistical modeling assumptions by preprocessing data with sophisticated matching techniques. MatchIt supports a wide range of matching methods, including exact, subclassification, nearest neighbor, optimal, and genetic matching. The software is designed for causal inference with a dichotomous treatment variable and pretreatment control variables, and can be used for both experimental and observational studies. MatchIt provides tools to assess balance, which is crucial for ensuring that the matching procedure effectively reduces confounding variables. After preprocessing, researchers can use any parametric model they would have used without MatchIt, but with more robust and less sensitive inferences. MatchIt is integrated with Zelig, an R package that simplifies the process of conducting parametric analyses on matched data. The package includes detailed documentation and examples to guide users through the matching and analysis process.
Reach us at info@study.space
[slides and audio] MatchIt%3A Nonparametric Preprocessing for Parametric Causal Inference