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Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others. One approach to this problem is through controlled experiments/trials - but controlled experiments are expensive. Hence it is important to choose the best policies on the basis of observational data. This presents two difficult challenges: (i) missing counterfactuals, and (ii) selection bias. This paper presents theoretical bounds on estimation errors of counterfactuals from observational data by making connections to domain adaptation theory. It also presents a principled way of choosing optimal policies using domain adversarial neural networks. We illustrate the effectiveness of domain adversarial training together with various features of our algorithm on a semi-synthetic breast cancer dataset and a supervised UCI dataset (Statlog).

Given the limited pool of donor organs, accurate predictions of survival on the wait list and post transplantation are crucial for cardiac transplantation decisions and policy. However, current clinical risk scores do not yield accurate predictions. We develop a new methodology (ToPs, Trees of Predictors) built on the principle that specific predictors should be used for specific clusters within the target population. ToPs discovers these specific clusters of patients and the specific predictor that perform best for each cluster. In comparison with current clinical risk scoring systems, our method provides significant improvements in the prediction of survival time on the wait list and post transplantation. For example, in terms of 3 month survival for patients who were on the US patient wait list in the period 1985 to 2015, our method achieves AUC of 0.847, the best commonly used clinical risk score (MAGGIC) achieves 0.630. In terms of 3 month survival/mortality predictions (in comparison to MAGGIC), holding specificity at 80.0 percents, our algorithm correctly predicts survival for 1,228 (26.0 percents more patients out of 4,723 who actually survived, holding sensitivity at 80.0 percents, our algorithm correctly predicts mortality for 839 (33.0 percents) more patients out of 2,542 who did not survive. Our method achieves similar improvements for other time horizons and for predictions post transplantation. Therefore, we offer a more accurate, personalized approach to survival analysis that can benefit patients, clinicians and policymakers in making clinical decisions and setting clinical policy. Because risk prediction is widely used in diagnostic and prognostic clinical decision making across diseases and clinical specialties, the implications of our methods are far reaching., Comment: Main manuscript: 20 pages, Supplementary materials: 13 pages, 5 figures, 3 tables. Submitted to Science Translational Medicine