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作者：Yin, Mingzhang; Shi, Claudia; Wang, Yixin; Blei, David M.
作者单位：State University System of Florida; University of Florida; Columbia University; University of Michigan System; University of Michigan; Columbia University
摘要：Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an assumption that is fundamentally untestable with observed data. To assess the robustness of individual-level causal conclusion with unconfoundedness, this paper proposes a method for sensitivity analysis of the ITE, a way to estimate a range of the ITE under unobserved confounding. The method we develop quantifies unme...
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作者：Shi, Chengchun; Luo, Shikai; Le, Yuan; Zhu, Hongtu; Song, Rui
作者单位：University of London; London School Economics & Political Science; Shanghai University of Finance & Economics; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University
摘要：We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications. Most of existing policy optimization algorithms in the computer science literature are developed in online settings where data are easy to collect or simulate. Their generalizations to mobile health applications with a pre-collected offline dataset remain unknown. The aim of this paper is to develop a novel advantage learning framework in order to ef...
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作者：Boldea, Otilia; Magnus, Jan R.
作者单位：Tilburg University; Vrije Universiteit Amsterdam; Tinbergen Institute
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作者：Li, Ting; Shi, Chengchun; Lu, Zhaohua; Li, Yi; Zhu, Hongtu
作者单位：Shanghai University of Finance & Economics; University of London; London School Economics & Political Science; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：Many modern tech companies, such as Google, Uber, and Didi, use online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on average treatment effects, situations with skewed and heavy-tailed outcome distributions may benefit from alternative criteria, such as quantiles. However, assessing dynamic quantile treatment effects (QTE) remains a challenge, particularly when dealing with data from ride-sourcing platforms that involve...
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作者：Shi, Peng; Zhang, Wei; Shi, Kun
作者单位：University of Wisconsin System; University of Wisconsin Madison; Northern Illinois University
摘要：In property insurance claims triage, insurers often use static information to assess the severity of a claim and to identify the subsequent actions. We hypothesize that the pattern of weather conditions throughout the course of a loss event is predictive of the insured losses, and hence appropriate use of weather dynamics improves the operation of insurers' claim management. To test this hypothesis, we propose a deep learning method to incorporate dynamic weather information in the predictive ...
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作者：Harshaw, Christopher; Saevje, Fredrik; Spielman, Daniel A.; Zhang, Peng
作者单位：Massachusetts Institute of Technology (MIT); Yale University; Rutgers University System; Rutgers University New Brunswick
摘要：The design of experiments involves a compromise between covariate balance and robustness. This article provides a formalization of this tradeoff and describes an experimental design that allows experimenters to navigate it. The design is specified by a robustness parameter that bounds the worst-case mean squared error of an estimator of the average treatment effect. Subject to the experimenter's desired level of robustness, the design aims to simultaneously balance all linear functions of pote...
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作者：Cui, Yifan; Hannig, Jan; Kosorok, Michael R.
作者单位：Zhejiang University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：Censored data, where the event time is partially observed, are challenging for survival probability estimation. In this article, we introduce a novel nonparametric fiducial approach to interval-censored data, including right-censored, current status, case II censored, and mixed case censored data. The proposed approach leveraging a simple Gibbs sampler has a useful property of being one size fits all, that is, the proposed approach automatically adapts to all types of noninformative censoring ...
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作者：Choi, David
作者单位：Carnegie Mellon University
摘要：In experiments that study social phenomena, such as peer influence or herd immunity, the treatment of one unit may influence the outcomes of others. Such interference between units violates traditional approaches for causal inference, so that additional assumptions are often imposed to model or limit the underlying social mechanism. For binary outcomes, we propose new estimands that can be estimated without such assumptions, allowing for interval estimates that assume only the randomization of...
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作者：Bing, Xin; Cheng, Wei; Feng, Huijie; Ning, Yang
作者单位：University of Toronto; Brown University; Microsoft; Cornell University
摘要：This article studies the inference of the regression coefficient matrix under multivariate response linear regressions in the presence of hidden variables. A novel procedure for constructing confidence intervals of entries of the coefficient matrix is proposed. Our method first uses the multivariate nature of the responses by estimating and adjusting the hidden effect to construct an initial estimator of the coefficient matrix. By further deploying a low-dimensional projection procedure to red...
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作者：Qian, Wei; Ing, Ching-Kang; Liu, Ji
作者单位：University of Delaware; National Tsing Hua University
摘要：This article studies an important sequential decision making problem known as the multi-armed stochastic bandit problem with covariates. Under a linear bandit framework with high-dimensional covariates, we propose a general multi-stage arm allocation algorithm that integrates both arm elimination and randomized assignment strategies. By employing a class of high-dimensional regression methods for coefficient estimation, the proposed algorithm is shown to have near optimal finite-time regret pe...
