If you are intrigued by "double-machine-learning" of Chernozhuov et al., but find it intimidating to read the paper arxiv.org/abs/1608.00060, these two slides explain the basic idea quite simply.
Key aspect is sample splitting: predicted Y & predicted D fcns are estimated in a auxiliary sample. Final reg in main sample.
This reduces bias.
But also reduces sample size.
So they do "cross-fitting": swap main & aux sample & repeat.
You get 2 estimates; take average.
For your Y & D predictions, pick your favorite ML tool (Random Forest, Boosting, LASSO). Or ensemble.
Tune the model within the auxilary sample (which has a training and testing subsamples)
Once ML part is done & you have your Y and D residuals, it's just a bivariate regression
Improves on double selection (DS) approach, also by Chernozhukov.
DS chooses covariates that *either* predict Y or D using LASSO on full sample, then do OLS of Y on D & chosen predictors.
DS relies on sparsity, needs LASSO.
DoubleML uses sample splitting, allows many ML tools.
DoubleML can be used in much more complicated setups than partial linear model.
E.g., can have heterogeneous treatment effects & non-linearities. Can use the ML-predicted value of D (propensity score) to reweight. Etc.
But wanted to convey that the basic idea is simple.