Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects

Understand

Conditional Average Treatment Effects (CATE) estimation is one of the main challenges in causal inference with observational data.

• In addition to Machine Learning based-models, nonparametric estimators called meta-learners have been developed to estimate the CATE with the main advantage of not restraining the estimation to a specific supervised learning method.
• This task becomes, however, more complicated when the treatment is not binary as some limitations of the naive extensions emerge.
• This paper looks into meta-learners for estimating the heterogeneous effects of multi-valued treatments.