Fair Adaptive Experiments
Waverly Wei, Xinwei Ma, Jingshen Wang

TL;DR
This paper introduces a fair adaptive experiment method that improves data efficiency, ensures envy-free treatment allocation, and enhances participant welfare without relying on parametric outcome models.
Contribution
It proposes a novel, non-parametric adaptive treatment assignment strategy that guarantees fairness and asymptotic optimality, addressing fairness concerns in adaptive experiments.
Findings
Theoretical convergence rate of treatment effect estimates is established.
The algorithm asymptotically approaches the optimal allocation rule.
Simulation results demonstrate improved fairness and efficiency.
Abstract
Randomized experiments have been the gold standard for assessing the effectiveness of a treatment or policy. The classical complete randomization approach assigns treatments based on a prespecified probability and may lead to inefficient use of data. Adaptive experiments improve upon complete randomization by sequentially learning and updating treatment assignment probabilities. However, their application can also raise fairness and equity concerns, as assignment probabilities may vary drastically across groups of participants. Furthermore, when treatment is expected to be extremely beneficial to certain groups of participants, it is more appropriate to expose many of these participants to favorable treatment. In response to these challenges, we propose a fair adaptive experiment strategy that simultaneously enhances data use efficiency, achieves an envy-free treatment assignment…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods in Clinical Trials · Health Systems, Economic Evaluations, Quality of Life
