Confidence Interval for categorical outcome about econml HOT 3 OPEN

If you just care about the ATE on the training set, then use the doubly robust ATE (which you can get programmatically from the ate_ attribute). The ate() method is more flexible, allowing you to also compute the ATE for other populations X, but it is not doubly-robust.

In terms of interpretation, a value of 0.313 means that increasing the probability of assigning an individual to treatment 5 instead of treatment 0 by p will increase the likelihood of a severe outcome by 0.313p. (This estimate is linear in the treatment probability which may not be completely realistic for a discrete outcome, since for some values of X we may have small variations in treatment that correspond to large variations in output, which would extrapolate to more than a 100% change in severity probability given a 100% change in treatment from one level to another, which is impossible)

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@kbattocchi Hi Keith, Thanks for the reply. I am working with accident data. The treatment variable taken here is the relative velocity and '5' indicates more than 80kmph and '0' is 20kmph. The target variable is injury severity. I expected a result that would say if relative velocity changes from 0 to 5, it would increases the injury severity probability by x. But the way you intrepreted is little different.

1. So the use case is not applicable here? In general, i want to analyse the parameters from the accident Database and its influence on injury severity which is a categorical variable.
2. As you mentioned , ATE here is linear, so should i use Treatment Featurizer?

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@ellpri I think that my answer is consistent with what you're looking for - changing 100% from '0' to '5' means changing the severity probability by 100% of 0.313, i.e. increasing it by 0.313. I only added the caveat because the linearity of the model is not necessarily completely realistic for discrete outcomes - we perform the estimate conditional on X by regressing the unexpected variation in outcome conditional on X and W to on the unexpected variation in treatment conditional on X and W, and empirically it's possible that for some X there was a big unexpected change in Y (there was a severe injury when we thought that was only 10% likely given X, say) but only a small unexpected change in T (the relative velocity was 5, and we thought that was 95% likely given X) - in that case it looks like a very small change in T leads to a big change in Y, which will extrapolate to a more than 100% change in outcome given a change in treatment from '0' to '5'. Despite this, empirically DML seems to generally perform well with discrete outcomes even though theoretically something like a "double machine learning for logistic regression" setup might be more appropriate.

The treatment featurizer won't affect this - you're already fitting a CATE model that is flexible in X (because you are using CausalForestDML), so featurizing X won't buy you anything - the linearity that I'm talking about is linearity in the treatment (probability). But discrete models are linear in the treatment without loss of generality.

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