different learners for different treatments in Simultaneous Inference

Hi,
I have an idea to develop the package for simultaneous inference.

When the nature of the treatments are different (continuous or binary) it is not possible to run the function DoubleMLPLR, for example. Because there is only one choice for the argument ml_m to estimate related nuisance function. To more elaborate,
consider two treatments d1 and d2 which are continuous and binary, respectively.
To estimate the nuisance function in the case of causal inference for d1 we must apply a machine learning method for family gaussian. While to for causal inference in the case of d2 we must apply a machine learning method for logistic regression. Thus, users must define a continuous version of d2 or convert d1 to a binary treatment to have a same-nature treatments.

However, in some cases, the program automatically detect the nature of the treatments (for example regr.gbm learner from the package gbm).

If the argument ml_m can be of type list as length as d_cols, we can run DoubleMLPLR for different-nature treatment situation.

Thanks for your hot pkg!