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Does Personalized Allocation Make Our Experimental Designs More Fair?


Bertille Picard
Doctorante à l'AMSE

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Algorithms can optimize treatment allocation within an experimental design. They can progressively identify the most beneficial treatment for the subjects and thus maximize the experiment’s overall impact. However, these designs raise concerns for experimentalists and policymakers because they involve transferring decision-making to an algorithm. Are adaptive experiments inherently fairer and thus a preferred choice over traditional randomized controlled trials? In this paper, I propose a comprehensive examination of fairness by considering multiple criteria that can influence the researchers’ preference for one design over the other: the possibility to increase the benefits of the experiment for the experimental subjects, the transparency of the decision rule, the absence of discrimination regarding the treatment allocation, the protection of individuals’ data. By summarizing and analyzing these distinct criteria through a utility model, I discuss the relative fairness of adaptive experiments and standard randomized controlled trials. Specifically, I show that these different designs align with extreme versions of the fairness utility model, reflecting the pursuit of distinct fairness objectives within experimental settings. I highlight intermediate solutions that can be pursued to reconcile and balance different fairness objectives in experimental designs.

Informations pratiques


Institut Agro de Montpellier / INRAE - Bat. 11, salle du Conseil, 2 Place Viala 34000 Montpellier, et en ligne

Dates et heure

27 Fév, 2024


Gabriel Bayle
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