Comparison of meta-learners for estimating multi-valued treatment
heterogeneous effects
- URL: http://arxiv.org/abs/2205.14714v3
- Date: Sat, 3 Jun 2023 13:29:50 GMT
- Title: Comparison of meta-learners for estimating multi-valued treatment
heterogeneous effects
- Authors: Naoufal Acharki and Ramiro Lugo and Antoine Bertoncello and Josselin
Garnier
- Abstract summary: Conditional Average Treatment Effects (CATE) estimation is one of the main challenges in causal inference with observational data.
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 paper looks into meta-learners for estimating the heterogeneous effects of multi-valued treatments.
- Score: 2.294014185517203
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. We consider
different meta-learners, and we carry out a theoretical analysis of their error
upper bounds as functions of important parameters such as the number of
treatment levels, showing that the naive extensions do not always provide
satisfactory results. We introduce and discuss meta-learners that perform well
as the number of treatments increases. We empirically confirm the strengths and
weaknesses of those methods with synthetic and semi-synthetic datasets.
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