propensity-scores

My R package WeightIt has a huge new update, making it one of the biggest updates since I started the project. Version 1.0.0 introduces a few breaking changes, including the possibility that old results will not align with results from newer version of the package.

As I’ve been studying M-estimation and the covariate balancing propensity score (CBPS) (Imai and Ratkovic 2014), I’ve been noticing some interesting connections between these methods and want to share them with you.

I’m often asked how the matching weights produced by MatchIt are computed. The weights are necessary for estimating the treatment effect in the matched sample; indeed, the weights determine the matched sample.

Multiply imputed data always makes things a little harder. Essentially, you have to perform each step of the analysis in each imputed dataset and then combine the results together in a special way.

Today I’m going to demonstrate performing a subgroup analysis after propensity score matching using R. Subgroup analysis, also known as moderation analysis or the analysis of effect modification, concerns the estimation of treatment effects within subgroups of a pre-treatment covariate.