Totte Harinen

The users are identified based on the predicted uplift. The proportion of users to be targeted can be based on external constraints (for example, you have a budget for targeting...

The model learns the features of the population that are predictive of the treatment effect, and if you are willing to assume that new users who come in are similar...

This is a great question. I'm sure that the effect of overfitting on CATE computation depends on the specific meta-learner that you are interested in. For example, the [X-learner paper](https://arxiv.org/pdf/1706.03461.pdf)...

Could you please give an example of the kind of use case that you're solving? "More accurate ATE" can mean a lot of different things. For example, are you dealing...

Regarding the first two questions, it is known that the T-learner suffers when treatment and control classes are imbalanced. In fact, the [X-learner](https://arxiv.org/pdf/1706.03461.pdf) is largely motivated by this observation. In...

As mentioned on #526, any correlation you see between features and predicted treatment effects is not necessarily causal. Regarding your second question, if you are simply interested in calculating treatment...

Yes, you can do that, although there is no benefit compared to the multiple regression approach mentioned above. If you decide to split the data yourself, you could use something...

Dropped some comments but overall looks great to me. The example notebook fails to render for me at the moment but I'll take a look if/when you've added more predictors...

When you say it can't be trained, what exactly happens when you try to do it?

It's expected that the T-Learner returns a number of control vectors corresponding to the number of treatments. This is because the implementation simply loops over each treatment and estimates a...