On the Distinction Between "Conditional Average Treatment Effects" (CATE) and "Individual Treatment Effects" (ITE) Under Ignorability Assumptions
This addresses a conceptual confusion in causal inference research, which is incremental but important for improving personalized treatment effect estimation methods.
The paper clarifies that under ignorability assumptions, methods claiming to estimate 'individual treatment effects' (ITE) often actually estimate 'conditional average treatment effects' (CATE), highlighting a distinction that could impede progress in personalized effect estimation.
Recent years have seen a swell in methods that focus on estimating "individual treatment effects". These methods are often focused on the estimation of heterogeneous treatment effects under ignorability assumptions. This paper hopes to draw attention to the fact that there is nothing necessarily "individual" about such effects under ignorability assumptions and isolating individual effects may require additional assumptions. Such individual effects, more often than not, are more precisely described as "conditional average treatment effects" and confusion between the two has the potential to hinder advances in personalized and individualized effect estimation.