There is a very good thread about Propensity Score Matching after multiple imputation with the articles referred:
In the refered articles, they talk about
- averaging of propensity scores after multiple imputation, followed by causal inference (method 2 in your post above)
- causal inference using each set of propensity scores from the multiple imputations followed by averaging of the causal estimates.
Method 2 is propensity score matching, lets say when someone imputes 5 datasets, in all 5 of them. However, we then end up with 5 propensity score matched cohorts and we want to make 1 propensity matched cohort of it; also how to implement this in SPSS/R, this stays unclear.
Some Articles also talk about the Rubin's Rule for pooling; but could not find good implentation in SPSS/R literature on that, if somebody could help on that I would appreciate it.
So in short, the question is: how to properly perform propensity score matching after multiple imputation and how to implement it in SPSS/R? If anybody has reference material, I would like to read it!