  We provide a general constrained risk inequality that applies to arbitrary
non-decreasing losses, extending a result of Brown and Low [Ann. Stat. 1996].
Given two distributions $P_0$ and $P_1$, we find a lower bound for the risk of
estimating a parameter $\theta(P_1)$ under $P_1$ given an upper bound on the
risk of estimating the parameter $\theta(P_0)$ under $P_0$. The inequality is a
useful pedagogical tool, as its proof relies only on the Cauchy-Schwartz
inequality, it applies to general losses, and it transparently gives risk lower
bounds on super-efficient and adaptive estimators.
