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Counterfactual Predictions under Runtime Confounding
Amanda Coston · Edward Kennedy · Alexandra Chouldechova

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1622

Algorithms are commonly used to predict outcomes under a particular decision or intervention, such as predicting likelihood of default if a loan is approved. Generally, to learn such counterfactual prediction models from observational data on historical decisions and corresponding outcomes, one must measure all factors that jointly affect the outcome and the decision taken. Motivated by decision support applications, we study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data, but it is infeasible, undesirable, or impermissible to use some such factors in the prediction model. We refer to this setting as runtime confounding. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting. Our theoretical analysis and experimental results suggest that our method often outperforms competing approaches. We also present a validation procedure for evaluating the performance of counterfactual prediction methods.

Author Information

Amanda Coston (Carnegie Mellon University)
Edward Kennedy (Carnegie Mellon University)
Alexandra Chouldechova (CMU)

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