Likelihood-free hypothesis testing

Consider the problem of binary hypothesis testing. Given $Z$ coming from either $\mathbb P^{\otimes m}$ or $\mathbb Q^{\otimes m}$, to decide between the two with small probability of error it is sufficient, and in many cases necessary, to have $m\asymp1/\epsilon^2$, where $\epsilon$ measures the separation between $\mathbb P$ and $\mathbb Q$ in total variation ($\mathsf{TV}$). Achieving this, however, requires complete knowledge of the distributions and can be done, for example, using the Neyman-Pearson test. In this paper we consider a variation of the problem which we call likelihood-free hypothesis testing, where access to $\mathbb P$ and $\mathbb Q$ is given through $n$ i.i.d. observations from each. In the case when $\mathbb P$ and $\mathbb Q$ are assumed to belong to a non-parametric family, we demonstrate the existence of a fundamental trade-off between $n$ and $m$ given by nm\asymp n_\sf{GoF}^2(\epsilon), where n_\sf{GoF}(\epsilon) is the minimax sample complexity of testing between the hypotheses $H_0:\, \mathbb P=\mathbb Q$ vs $H_1:\, \mathsf{TV}(\mathbb P,\mathbb Q)\geq\epsilon$. We show this for three families of distributions, in addition to the family of all discrete distributions for which we obtain a more complicated trade-off exhibiting an additional phase-transition. Our results demonstrate the possibility of testing without fully estimating $\mathbb P$ and $\mathbb Q$, provided $m \gg 1/\epsilon^2$.