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Personalized treatment effect estimates are often of interest in high-stakesapplications -- thus, before deploying a model estimating such effects inpractice, one needs to be sure that the best candidate from the ever-growingmachine learning toolbox for this task was chosen. Unfortunately, due to theabsence of counterfactual information in practice, it is usually not possibleto rely on standard validation metrics for doing so, leading to a well-knownmodel selection dilemma in the treatment effect estimation literature. Whilesome solutions have recently been investigated, systematic understanding of thestrengths and weaknesses of different model selection criteria is stilllacking. In this paper, instead of attempting to declare a global `winner', wetherefore empirically investigate success- and failure modes of differentselection criteria. We highlight that there is a complex interplay betweenselection strategies, candidate estimators and the data used for comparingthem, and provide interesting insights into the relative (dis)advantages ofdifferent criteria alongside desiderata for the design of further illuminatingempirical studies in this context.

In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation