Closed lorenzoFabbri closed 8 months ago
I assume by trimming you mean setting extreme weights to a less extreme value. If so, that is just a modeling decision, no different from choosing probit vs logit regression. So you can trim at any point in the analysis, just the same as you would by choosing a different model. Do whatever it takes to achieve balance and a high ESS. There are no rules otherwise.
This is another mostly conceptual question, and not a bug/issue with the package.
I am doing sensitivity analysis on a small subset of my sample. I thus decided to estimate the weights to reduce the effects of any selection bias:
where
selction
is a indicator variable which is 1 when the subject was indeed selected into the subset, 0 otherwise. I then pass theweights
from the call toWeightIt::weightit
tos.weights
when estimating the actual balancing weights.I was wondering whether it is better to eventually trim the weights for selection bias, or if it is better to pass them as is to the second step and then trim these final balancing weights. I know I can check the summary and see whether there are extreme weights, but I was looking for a more conceptual understanding. Thank you.