Guidance for using propensity score matching in R

I am new to programming as well as econometrics and would like to ask some guidance for learning.

I am planning to calculate the effectiveness of a public works scheme in country A. By effectiveness I mean the rate of success by which participants were able to return to the open labour market compared to registered job seekers who did not participate in the programme.

I have found out from economists that for this type of analysis I can use propensity score matching (PSM) if I have panel microdata. I have already submitted a data request to public authorities, and I expect to receive a tidy, unbalanced dataset with several thousand observations for the past three years.

I was also recommended some books from which I could learn how to conduct the analysis (Woolridge 2012, World Bank 2009). All of these use Stata, but, if possible, I would prefer to stay with R that I have been learning since the past year. Unfortunately, however, I have not found a book yet that would comprehensively treat PSM in R (eg. among the useR series).

I am a beginner in R. At the moment, I can manipulate data by indexing, but not yet by loops. In order to avoid the risk of taking on something that I am unable to do, I would like to ask whether I can expect to conduct such an analysis by basic R programming skills, and reading about packages on panel data (such as plm) and PSM (eg. MatchIt), or it is essential that I also learn some more advanced skills before, such as loops, apply, or writing functions? If the latter, then concretely which ones?


Less is more. Stay pure. Stay poor.
I don't think you need to do any loops in MatchIt. Tell us more about how or by what you want to match observations!!!

I am familiar with PSM scores and stratum in other programs. I know Gary King has an YouTube video talking about why you shouldn't exact match and has a coarse matching package maybe. I think the thing was you through away too much data in exact matching. Looking forward to hearing more about your project.