Hello,
I had two questions regarding using the CEM package in Stata that I would be very greatful if someone helped me with.
1) In the 2009 Stata journal (cem: Coarsened exact matching in Stata), specifically the part of 4.7 Using cem to improve other matching methods,
it says that after CEM, PSM(propensity score matching) can be used to obtain better inferences.
I was wondering if this applied to propensity score weighting as well as propensity score weighting has the disadvantage of producing "bad" weights for outliers I believe. (In short, I want to know if I can conduct CEM and then using the data, conduct propensity score weighting-in order to get rid of much bias as possible)
Moreover, if this works, would I add the same covariates used in CEM and propensity score weighting in the final analysis model?
2) Also, when using CEM, the weights are incorporated in following analysis using the "iweights" option in Stata,
However, in propensity score weigthing, "pweights" seem to be the norm.
Is there a reason why the two methods use different weights?
The results of iweights and pweights differ for both methods and I was confused.
I am not familiar with weights, so I might be asking a silly question but I was curious.
I know it is a lot of questions, but would really appreciate it if someone could help me out.
Thank you in advance.
