Propensity Score Matching In R

Propensity score matching psm paul r.

Propensity score matching in r.

Matching is based on propensity scores estimated with logistic regression. If matching is done well the treatment and control groups will have near identical means of each covariate at each value of the propensity score. Rosenbaum and rubin 1983 is the most commonly used matching method possibly even the most developed and popular strat egy for causal analysis in observational studies pearl 2010. Psm attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus those that did not.

The package provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. The concept of propensity score matching psm was first introduced by rosenbaum and rubin 1983 in a paper entitled the central role of the propensity score in observational studies for casual effects statistically it means. Proper citations of these r packages is provided in the program. This website is for the distribution of matching which is a r package for estimating causal effects by multivariate and propensity score matching.

According to wikipedia propensity score matching psm is a statistical matching technique that attempts to estimate the effect of a treatment policy or r bloggers r news and tutorials contributed by hundreds of r bloggers. It is used or referenced in over 127 000 scholarly articles 1. Here i use a loess smoother to estimate the mean of each covariate by treatment status at each value of the propensity score. The output below indicates that the propensity score matching creates balance among covariates controls as if we were explicitly trying to match on the controls themselves.

Using the spss r plugin the software calls several r packages mainly matchit and optmatch. See previous post on propensity score analysis for further details. Below is an example using the four covariates in our model.