Doubly Robust Counterfactual Classification

• URL: http://arxiv.org/abs/2301.06199v1
• Date: Sun, 15 Jan 2023 22:04:46 GMT
• Title: Doubly Robust Counterfactual Classification
• Authors: Kwangho Kim, Edward H. Kennedy, Jos\'e R. Zubizarreta
• Abstract summary: 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.
• Score: 1.8907108368038217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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