Keith Battocchi

If you only care about point estimates, then I think you could use a lightgbm regressor as the `final_model` for a `DRLearner`. The advantage of the `RegressionForest` used as the...

Just to make completely sure I understand the question, you state that you > would like to estimate the CATE for the variable 'komplex' (discret) on 'dlz_implementierung' (continuous) based on...

I can't think of a clean way to do this exactly as you're suggesting, but I think that one alternative might be to pass all of the outcome metrics as...

Both X and W are used to predict both the outcome (Y) and the treatment (T). The difference is that we assume that only the features X have an effect...

@ggiannarakis This is a good question; I believe the same logic applies to the heterogeneous treatment effect setting that we use, so you'd want to include variables that affect the...

It might help if you could further describe your scenario - what do you mean by "refitting each time"? Each of our models should support pickling, assuming that the models...

Could you clarify what you mean? It should be straightforward to use deep learning for the first stages already - for example you could pass an `sklearn.neural_network.MLPRegressor` for the Y...

This is a great suggestion. In the meantime, note that you can read the optimized hyperparameters out of the class itself (e.g. if you are using `params='auto'`, then read off...

Thanks for the report. @moprescu - would it make sense to provide a discrete treatment option for the metalearners like we do for other estimator types?

@fullflu We had a brief discussion offline. You're right that right now the user is responsible for passing regression models rather than classification models when predicting outputs. We have a...