Heterogeneous treatment effects with experimental data – the uplift odyssey
Modeling treatment effects with experimental data is usually slightly different in spirit from working with observational data. This stems from the fact that experimental data is assumed to be unconfounded by design (assuming our experimental design and implementation were not flawed).
In this section, we’ll walk through a workflow of working with experimental data using EconML. We’ll learn how to use EconML’s basic API and see how to work with discrete treatments that have more than two levels. Finally, we’ll use some causal model evaluation metrics in order to compare the models.
The title of this section talks about heterogeneous treatment effects – we already know what they are, but there’s also a new term: uplift. Uplift modeling and heterogeneous (aka conditional) treatment effect modeling are closely related terms. In marketing and medicine, uplift...
