Is it possible to predict on new data using PLS SEM? Using the seminr package in R, I have fitted a model based on PLS SEM with several exogenous (latent) variables and one endogenous latent variable (ELV) measured by three indicators.
Beyond the usual out-of-sample evaluation commonly done in the PLS SEM framework, I would like to use the model to predict the construct score for the ELV (or the values of its indicators, alternatively) for new samples where the new samples do not contain data on the ELV and/or its indicators (similar to a common machine learning model). Is this possible or not?
I have assumed that this should be possible as PLS SEM's "predictive capabilities" are highlighted in relevant literature (and because of the fact that out-of-sample evaluation is a common prodecure in PLS SEM). I took a look at the seminr source code (in particular, the pls_predict and related functions) but maybe I am moving into a dead end here.

Update: I think I found a solution for the problem (at least for predicting indicators). It seems a bit hacky but it should work.
It is sufficient to set the values for all indicators in the testing set that should be predicted to 0 - essentially simulating a testing set without "labels" (other arbitrary values might work, as well; if the columns for these indicators do not exist in the testing set, they should simply be added). Next, estimate the model on the training set using estimate_pls and predict on the testing data using the predict method. To check that this approach works, the testing set can be passed without setting the indicators that should be predicted to 0 (assuming that the testing set contains "labels"). Predictions should be the same in both cases.
