  Inverse classification uses an induced classifier as a queryable oracle to
guide test instances towards a preferred posterior class label. The result
produced from the process is a set of instance-specific feature perturbations,
or recommendations, that optimally improve the probability of the class label.
In this work, we adopt a causal approach to inverse classification, eliciting
treatment policies (i.e., feature perturbations) for models induced with causal
properties. In so doing, we solve a long-standing problem of eliciting
multiple, continuously valued treatment policies, using an updated framework
and corresponding set of assumptions, which we term the inverse classification
potential outcomes framework (ICPOF), along with a new measure, referred to as
the individual future estimated effects ($i$FEE). We also develop the
approximate propensity score (APS), based on Gaussian processes, to weight
treatments, much like the inverse propensity score weighting used in past
works. We demonstrate the viability of our methods on student performance.
