Heterogeneous Treatment Effects
Interpretable and Validatable Uplift Modeling
In this note, we introduce a method for interpreting and validating the results of uplift modeling. We propose two novel strategies for controlling the Familywise Error Rate in this setting.
User Segmentation from Heterogeneous Treatment Effects
Imagine we are attempting to identify segments within an audience, perhaps so we can market to them more effectively through personalization. A common approach to doing so is to apply some kind of clustering algorithm (such as K-means) based on various user covariates. I have never been especially happy with this approach: the resulting clusters seem pretty arbitrary.
Heterogeneous Treatment Effect Estimation: Function Approximation
A simple approach to heterogeneous treatment effect estimation relies on a difference in approximations to the outcome function among the two treatment groups. In this post, I derive the conditions under which this approach works.