uhm... i think there's a small mistake on your understanding of how multiple imputation works. when they talk about pooled parameter estimates they don't mean that somehow the multiply-imputed datasets are "pooled" and then the analysis is ran on that one data set. if you use multiple imputation it generates several datasets, it fits the model you're specifying to each dataset and then those parameter estimates are pooled together, according to Rubin's rules for the estimates and their standard errors.

so there is no pooled dataset, only pooled parameter estimates (and their uncertainties).

but you mentioned that you tried to use Stata and LISREL. why aren't you using the gllamm package from Stata or LISREL instead of smartPLS? they both handle multiple imputation and FIML for missing data.

the whole field of PLS as a substitute to traditional covariance modeling is... well, quite fishy to be honest. from what i've learnt about it, i see limited use in their methods and a lot of questionable assumptions.