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Title: Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to individual causal effect predictions and is key for personalized assessments. Recent work has focused on how to learn CATE under unconfoundedness, i.e., when there are no unobserved confounders. Since CATE may not be identified when unconfoundedness is violated, we develop a functional interval estimator that predicts bounds on the individual causal effects under realistic violations of unconfoundedness. Our estimator takes the form of a weighted kernel estimator with weights that vary adversarially. We prove that our estimator is sharp in that it converges exactly to the tightest bounds possible on CATE when there may be unobserved confounders. Further, we study personalized decision rules derived from our estimator and prove that they achieve optimal minimax regret asymptotically. We assess our approach in a simulation study as well as demonstrate its application in the case of hormone replacement therapy by comparing conclusions from a real observational study and clinical trial.  more » « less
Award ID(s):
1656996
NSF-PAR ID:
10092364
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
89
ISSN:
2640-3498
Page Range / eLocation ID:
2281-2290
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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