Posts

A patient comes into urgent care presenting with an unpleasant rash on his arm. “Fortunately, we have a treatment,” says the doctor, “but it is expensive, has unpleasant side effects, and isn’t guaranteed to work.

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.

Genetic matching sounds cool and science-y, something we social scientists love because nobody thinks what we do is “real” science. And genetic matching is cool and science-y, but not because it has anything to do with genes or DNA.

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.