Orthogonal/Double ML: interaction of multiple treatments academic reference?

Hello,

Would like to know if there is any academic paper or sources that include the proof to show that it's eligible to create interaction terms of two different treatment variables in each stage of Double ML?

I noticed this section "What if I have many treatments?" in EconML the Double ML user guide. It suggests:

"The method is going to assume that each of these treatments enters linearly into the model. So it cannot capture complementarities or substitutabilities of the different treatments. For that you can also create composite treatments that look like the product of two base treatments. Then these product will enter in the model and an effect for that product will be estimated. This effect will be the substitute/complement effect of both treatments being present, i.e. If your treatments are too many, then you can use the SparseLinearDMLCateEstimator. However, this method will essentially impose a regularization that only a small subset of them has any effect."

Just want to see if you can help point to any academic or paper reference for above way to handle treatment interaction? Thank you!

Start with the “What if I have many treatments?” section of the EconML Double ML user guide and review the issue’s description of composite treatments. Done would mean identifying an appropriate academic reference or source that supports the treatment-interaction approach and adding or reporting that reference for the guide.