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The aim is to develop minimax regret treatment rules when dealing with finite samples, specifically focusing on quantiles of outcome distributions.

August 7, 2026Econometric TheoryOpen Access

Minimax Regret Treatment Rules With Finite Samples When a Quantile Is the Object of Interest

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The aim is to develop minimax regret treatment rules when dealing with finite samples, specifically focusing on quantiles of outcome distributions.
Analyzed various sampling schemes with fixed numbers of treated and untreated units.
Explored random treatment assignment with different assignment probabilities.
Included cases with known quantiles of the untreated population.
In cases with fixed treated and untreated units, any treatment rule is minimax regret optimal.
If the known quantile exceeds 1/2, the optimal rule is to never treat.
If the known quantile is below 1/2, the optimal rule is to always treat.

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Randomized trial explores treatment rules based on quantiles in finite samples, indicating optimal strategies depend on outcome quantiles.

Guggenberger et al. (2026) studied this question.

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Randomized trial explores treatment rules based on quantiles in finite samples, indicating optimal strategies depend on outcome quantiles.