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Fast Rates for Contextual Linear Optimization
Incorporating side observations of predictive features can help reduce u...
11/05/2020 ∙ by Yichun Hu, et al. ∙
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Stochastic Optimization Forests
We study conditional stochastic optimization problems, where we leverage...
08/17/2020 ∙ by Nathan Kallus, et al. ∙
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On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
We study the problem of estimating treatment effects when the outcome of...
03/27/2020 ∙ by Nathan Kallus, et al. ∙
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Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond
We consider the efficient estimation of a low-dimensional parameter in t...
12/30/2019 ∙ by Nathan Kallus, et al. ∙
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Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes
We study a nonparametric contextual bandit problem where the expected re...
09/05/2019 ∙ by Yichun Hu, et al. ∙
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Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
The increasing impact of algorithmic decisions on people's lives compels...
06/01/2019 ∙ by Nathan Kallus, et al. ∙
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Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
Assessing the fairness of a decision making system with respect to a pro...
11/27/2018 ∙ by Jiahao Chen, et al. ∙
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Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
We study the problem of learning conditional average treatment effects (...
10/05/2018 ∙ by Nathan Kallus, et al. ∙
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Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Valid causal inference in observational studies often requires controlli...
06/03/2018 ∙ by Nathan Kallus, et al. ∙
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