Closed dannysack closed 2 years ago
Hi Danny,
For continuous treatments, I don't include a generalized propensity score (GPS) in the output because it doesn't have the same interpretation as a propensity score for categorical treatments and can't really be used like the propensity score in regression adjustment as you are proposing. See Hirano & Imbens (2005). If you want to implement a Hirano & Imbens style GPS regression, you will have to program it yourself or look in another package. If you want to perform a sensitivity analysis, I recommend using other types of weights rather than other methods of adjusting for the GPS.
Thanks, Noah!
Hey, this is an excellent package, thank you so much!
I am trying to do a sensitivity analysis where I compare the effect estimate generated from the weight them object for a continuous exposure weighted sample to the effect estimate that includes the propensity score as a covariate (without weighing it). I am doing so using imputed data, however, using
weightthem
. Even after includinginclude.obj = TRUE
as suggested in #3, I do not see any components of the output object that include propensity scores for such a sensitivity analysis (I am posting here because theweightthem
documentation suggests that additional arguments beyond what is specified in the documentation are implemented viaweightit
).As an example:
Generate weights:
ols_wt <- weightthem(X ~ Z1 + Z2 + Z3, datasets = imp_data, method = "ps", approach = "within")
Model 1:
with(ols_wt, glm(Y ~ X, family = binomial))
Model 2:
with(imp_data, glm(Y ~ X + PS, family = binomial))
Does the propensity score exist in the
weightthem
object or will I just need to create it myself (which is not a problem, I just want to make sure I'm not missing anything and would rather use the propensity score used to generate the weights so I know I'm comparing related quantities)?Thanks so much!
Danny