  Many contemporary large-scale applications involve building interpretable
models linking a large set of potential covariates to a response in a nonlinear
fashion, such as when the response is binary. Although this modeling problem
has been extensively studied, it remains unclear how to effectively control the
fraction of false discoveries even in high-dimensional logistic regression, not
to mention general high-dimensional nonlinear models. To address such a
practical problem, we propose a new framework of $model$-$X$ knockoffs, which
reads from a different perspective the knockoff procedure (Barber and Cand\`es,
2015) originally designed for controlling the false discovery rate in linear
models. Whereas the knockoffs procedure is constrained to homoscedastic linear
models with $n\ge p$, the key innovation here is that model-X knockoffs provide
valid inference from finite samples in settings in which the conditional
distribution of the response is arbitrary and completely unknown. Furthermore,
this holds no matter the number of covariates. Correct inference in such a
broad setting is achieved by constructing knockoff variables probabilistically
instead of geometrically. To do this, our approach requires the covariates be
random (independent and identically distributed rows) with a distribution that
is known, although we provide preliminary experimental evidence that our
procedure is robust to unknown/estimated distributions. To our knowledge, no
other procedure solves the $controlled$ variable selection problem in such
generality, but in the restricted settings where competitors exist, we
demonstrate the superior power of knockoffs through simulations. Finally, we
apply our procedure to data from a case-control study of Crohn's disease in the
United Kingdom, making twice as many discoveries as the original analysis of
the same data.
