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Keywords: matching methods, causal inference, balance, preprocessing, R. 1. Introduction 1.1. What MatchIt does MatchIt implements the suggestions ofHo, Imai, King, and Stuart(2007) for improving parametric statistical models and reducing model dependence by preprocessing data with Many statistical packages offer tools for propensity score matching, and, for R users, the MatchIt package has been a staple since the release of its companion article, “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference” by Ho, Imai, King, and Stuart in 2007. However, most matching algorithms adopt slightly more complex methods. The method we use below is to find pairs of observations that have very similar propensity scores, but that differ in their treatment status. We use the package MatchIt for this.

What MatchIt does MatchIt implements the suggestions ofHo, Imai, King, and Stuart(2007) for improving parametric statistical models and reducing model dependence by preprocessing data with Many statistical packages offer tools for propensity score matching, and, for R users, the MatchIt package has been a staple since the release of its companion article, “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference” by Ho, Imai, King, and Stuart in 2007. However, most matching algorithms adopt slightly more complex methods. The method we use below is to find pairs of observations that have very similar propensity scores, but that differ in their treatment status. We use the package MatchIt for this. This package estimates the propensity score in the background and then matches observations R: Propensity Score Matching using MatchIt. How to specify desired matching accuracy for different covariates? Once we implement matching in R, the output provides comparisons between the balance in covariates for the treatment and control groups before and after matching.

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(2007). Dear Friends, I'm using Matchit package for a Cohor Study. After match the number provided by the summary output apparently don't correspond to the true value: Here the values provided by summary:>summary (test) Summary of Balance for Matched Data Age: Means treated 44.89; Means control 45.47 However, after applying a test (for instance) I get: > mean in group control: 47.47 and for treated Is > there some workaround or is my thinking mistaken? > > > > On Tue, Jan 3, 2017 at 8:55 AM, Gary King wrote: > >> Hi Juan, You could ask matchit for the lowest imbalance on a "greedy" >> basis, say 25 treated units with the closest controls or some such.

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