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.