MatchIt is an R package that implements nonparametric preprocessing methods to improve parametric statistical models and reduce dependence on statistical modeling assumptions in causal inference. It allows researchers to preprocess data using nonparametric matching methods, making causal inferences more robust and less sensitive to modeling assumptions. MatchIt works with R and seamlessly integrates with Zelig. The software includes a wide range of matching methods, including exact, subclassification, nearest neighbor, optimal, and genetic matching. These methods help balance covariate distributions between treated and control groups, improving the validity of causal inferences. MatchIt also provides tools to assess balance after matching, such as numerical summaries and graphical diagnostics. After preprocessing, researchers can use any parametric model they would have used without MatchIt, resulting in more robust inferences. MatchIt is designed for causal inference with a dichotomous treatment variable and pretreatment control variables. It can be used for other types of causal variables by dichotomizing them. MatchIt is particularly useful for observational studies where treatment is not randomly assigned. The package includes functions for conducting analyses after matching, including estimating average treatment effects on the treated (ATT) and other quantities of interest. MatchIt also provides options for checking balance, conducting analyses, and using Zelig for further statistical modeling. The software is flexible, allowing users to customize matching methods and assess the impact of different preprocessing strategies on causal inference.MatchIt is an R package that implements nonparametric preprocessing methods to improve parametric statistical models and reduce dependence on statistical modeling assumptions in causal inference. It allows researchers to preprocess data using nonparametric matching methods, making causal inferences more robust and less sensitive to modeling assumptions. MatchIt works with R and seamlessly integrates with Zelig. The software includes a wide range of matching methods, including exact, subclassification, nearest neighbor, optimal, and genetic matching. These methods help balance covariate distributions between treated and control groups, improving the validity of causal inferences. MatchIt also provides tools to assess balance after matching, such as numerical summaries and graphical diagnostics. After preprocessing, researchers can use any parametric model they would have used without MatchIt, resulting in more robust inferences. MatchIt is designed for causal inference with a dichotomous treatment variable and pretreatment control variables. It can be used for other types of causal variables by dichotomizing them. MatchIt is particularly useful for observational studies where treatment is not randomly assigned. The package includes functions for conducting analyses after matching, including estimating average treatment effects on the treated (ATT) and other quantities of interest. MatchIt also provides options for checking balance, conducting analyses, and using Zelig for further statistical modeling. The software is flexible, allowing users to customize matching methods and assess the impact of different preprocessing strategies on causal inference.