Propensity Score Matching on demographic baseline

A client asks for a PSM on gender for their big dataset of >10000 cases.

About 20 variables are supposed to be included, most of them binomial.

They hypothesize, that a certain treatment has worse outcomes in women, because of unequally shared comorbidites. I already advocated them on using interaction terms instead, yet they demand PSM.

For these about 20 variables observations vary a lot with more often than not ~x*10^1 observations for single variables only.

PSM not only seems to not work completely, but also loses about 90% of data and needs huge caliper widths.

Has anyone advice on this matter / can anyone criticize methodology etc.? Is it reasonable to match on gender, as it's independent and not a treatment decision? I am matching ATT, should I use another methodology (Inverse Probability Weighting or ATC)?

Any kind of comment, advise, opinion is appreciated even based on experience without references.

• You might be interested in this paper: gking.harvard.edu/files/gking/files/psnot.pdf – TPM Nov 23 '18 at 21:01
• Thanks for the response. Sadly I have realized statisticians are quick in critizing, but problem solving comes somewhat short at times. I also feel weighting is a cleaner solution than matching, yet not necessary more robust. It's all a big mess. – Nuke Nov 25 '18 at 21:46

PSM is a fine strategy, but others, including regression or weighting, work fine as well. I'm partial to entropy balancing, a form of weighting that yields exact balance on the covariates while preserving sample size. You can access it using the WeightIt package in R.