# Propensity score matching and unbalanced co-variables in IPWT

When using propensity score (PS) for calculating inverse probability weighting (IPW) in an average treatment effect (ATE) approach, is it valid to remove from the PS those co-variables that remain unbalanced, and then, when performing the final analyses using them as co-variables together with the treatment variable?

EDIT: Since the original text may be confusing, I would try to clarify it.

I have used ps() function from twang R package, which implements GBM models. Once executed, there are some co-variables that have a higher absolute standardized effect size in the balanced data than in the unweighted original.

My question was, Does it make sense to remove the co-variables that will be unbalanced after calculating ps(), and then, when performing a logistic regression with the weighted data add them as co-variables?

From @Noah answer: Is statistical valid (and also makes sense), to use all co-variables for calculating the weights, and then used unbalanced co-variables in the final logistic regression analyses?

You should also consider using either optimization-based approaches like entropy balancing, which guarantee balance on the covariate means and have good efficiency properties, or machine learning methods like generalized boosted modeling (GBM) or Bayesian additive regression trees (BART), which attempt to flexibly model the propensity score. These are available in the R package WeightIt (which I developed). There has been so much work done on new, robust methods with excellent statistical properties that one should not be using the simple methods developed 20 years ago.