Testing Monotonicity in a Finite Population

Jiafeng Chen, Jonathan Roth and Jann Spiess

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Abstract: We consider the extent to which we can learn from a completely randomized experiment whether all individuals have treatment effects that are weakly of the same sign, a condition we call monotonicity. From a classical sampling perspective, it is well-known that monotonicity is not falsifiable. We show that from the design-based perspective---in which the potential outcomes are fixed and only treatment assignment is stochastic---that the distribution of treatment effects in the finite population (and hence whether monotonicity holds) is formally identified. Nevertheless, we show that the scope for learning about violations of monotonicity is severely limited. Frequentist tests of monotonicity have generically poor power, and there exist (non-degenerate) Bayesian priors that never update about whether monotonicity holds. Estimators of the magnitude of the violation of monotonicity are likewise shown to have poor minimax mean-squared error.

Date: 2025-12, Revised 2026-08
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