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The need to evaluate treatment effectiveness is ubiquitous in most ofempirical science, and interest in flexibly investigating effect heterogeneityis growing rapidly. To do so, a multitude of model-agnostic, nonparametricmeta-learners have been proposed in recent years. Such learners decompose thetreatment effect estimation problem into separate sub-problems, each solvableusing standard supervised learning methods. Choosing between differentmeta-learners in a data-driven manner is difficult, as it requires access tocounterfactual information. Therefore, with the ultimate goal of buildingbetter understanding of the conditions under which some learners can beexpected to perform better than others a priori, we theoretically analyze fourbroad meta-learning strategies which rely on plug-in estimation andpseudo-outcome regression. We highlight how this theoretical reasoning can beused to guide principled algorithm design and translate our analyses intopractice by considering a variety of neural network architectures asbase-learners for the discussed meta-learning strategies. In a simulationstudy, we showcase the relative strengths of the learners under differentdata-generating processes.

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms