Experimental Evaluation of Individualized Treatment Rules
This work addresses the need for robust evaluation of personalized treatment rules for policy makers, though it is incremental as it builds on existing frameworks like uplift modeling.
The authors tackled the problem of empirically evaluating individualized treatment rules (ITRs) by proposing new metrics, the population average prescriptive effect (PAPE) and area under the prescriptive effect curve (AUPEC), which can be estimated without modeling assumptions or asymptotic approximations, making them applicable to any ITR including those based on complex machine learning algorithms.
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a target population. We propose a new evaluation metric, the population average prescriptive effect (PAPE). The PAPE compares the performance of ITR with that of non-individualized treatment rule, which randomly treats the same proportion of units. Averaging the PAPE over a range of budget constraints yields our second evaluation metric, the area under the prescriptive effect curve (AUPEC). The AUPEC represents an overall performance measure for evaluation, like the area under the receiver and operating characteristic curve (AUROC) does for classification, and is a generalization of the QINI coefficient utilized in uplift modeling. We use Neyman's repeated sampling framework to estimate the PAPE and AUPEC and derive their exact finite-sample variances based on random sampling of units and random assignment of treatment. We extend our methodology to a common setting, in which the same experimental data is used to both estimate and evaluate ITRs. In this case, our variance calculation incorporates the additional uncertainty due to random splits of data used for cross-validation. The proposed evaluation metrics can be estimated without requiring modeling assumptions, asymptotic approximation, or resampling methods. As a result, it is applicable to any ITR including those based on complex machine learning algorithms. The open-source software package is available for implementing the proposed methodology.