Prophit: Causal inverse classification for multiple continuously valued treatment policies
This work addresses a long-standing issue in causal inference for personalized recommendations, though it appears incremental as it builds on existing inverse classification and propensity score methods.
The paper tackles the problem of eliciting multiple, continuously valued treatment policies in inverse classification by adopting a causal approach, introducing the ICPOF framework and iFEE measure, and demonstrating viability on student performance data.
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.