different learners for different treatments in Simultaneous Inference

[doubleml]

different learners for different treatments in Simultaneous Inference

[PhilippBach]

Hi @hadigilan,

thanks a lot for your interest in the package and for your suggestions. We discussed your idea and we agree that it would be great to have more flexibility with regard to different learners/target variables. We add this point to the list of features that we will implement in the future. Because the feature is related to handling the learners, we have to take care for potential side effects and other dependencies -> it will probably take a little while until we support this feature.

However, there are 2 potential work arounds that might help you already

1. You can use Regression Learners for the binary output which might already help a bit, like regr.gbm, i.e., with option distribution = "gaussian".

2. You can separately set up DoubleMLPLR objects for the continuous (d1) and the binary (d2) treatment variables and estimate the causal effects separately. Then you can manually merge the scores as if they would be obtained in the multiple treatment case and manually run the bootstrap code .

3. is probably much easier and quicker but neglets the binary nature of the treatment variable. 2) will probably be closer to what you want to do but involves more implementation effort. In case you go for 2) and do some proper implementation rather than a quick work around, you can open a PR and we can integrate it in the package.

Once more, thank you very much.

Best,

Philipp