Skip to yearly menu bar Skip to main content


Doubly Robust Counterfactual Classification

Kwangho Kim · Edward Kennedy · Jose Zubizarreta

Hall J (level 1) #304

Keywords: [ Causal Inference ] [ semiparametric theory ] [ counterfactual prediction ]

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.

Chat is not available.