Doubly Robust Counterfactual Classification
This provides a new tool for decision-making in fields like recidivism risk prediction, though it appears incremental as an extension of doubly-robust methods to classification.
The authors tackled counterfactual classification for decision-making under hypothetical scenarios by proposing a doubly-robust nonparametric estimator that incorporates flexible constraints, showing it achieves robust performance against model misspecification and fast √n convergence rates with tractable inference.
We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios. We propose a doubly-robust nonparametric estimator for a general counterfactual classifier, where we can incorporate flexible constraints by casting the classification problem as a nonlinear mathematical program involving counterfactuals. We go on to analyze the rates of convergence of the estimator and provide a closed-form expression for its asymptotic distribution. Our analysis shows that the proposed estimator is robust against nuisance model misspecification, and can attain fast $\sqrt{n}$ rates with tractable inference even when using nonparametric machine learning approaches. We study the empirical performance of our methods by simulation and apply them for recidivism risk prediction.