ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Hi,
I saw that now DML (I'm using Linear) gives the possibility to select "discrete outcome=True", this means than now residuals of outcome model are not more only {-1,0,1} and then I can use a classifier like XGBClassifier() for outcome model?
thanks
That's correct - if you set discrete_outcome=True then your y_model should be an sklearn classifier rather than a regressor and we'll use predict_proba rather than predict, so the residuals can take on any values in the range [-1,1].
kbattocchi
commented
4 months ago
Hi, I saw that now DML (I'm using Linear) gives the possibility to select "discrete outcome=True", this means than now residuals of outcome model are not more only {-1,0,1} and then I can use a classifier like XGBClassifier() for outcome model? thanks