Covariate-dependent missing at random ($f(R\mid Y^o,Y^m,X)=f(R\mid Y^o,X)$)

For each fixed observation pattern, the probability of that pattern may depend on observed response components and fully observed covariates, but not on the response components missing under that pattern. Conditional missing completely at random is the special case with no dependence on observed response components either.