-
作者：Chen, Zhe; Li, Xinran
作者单位：University of Pennsylvania; Pennsylvania Medicine; University of Chicago
摘要：Understanding treatment effect heterogeneity has become increasingly important in many fields. In this article we study distributions and quantiles of individual treatment effects to provide a more comprehensive and robust understanding of treatment effects beyond usual averages, although they are more challenging to infer due to nonidentifiability from the observed data. Recent randomization-based approaches offer finite-sample valid inference for treatment effect distributions and quantiles ...
-
作者：Rosenbaum, Paul R.
作者单位：University of Pennsylvania
摘要：Does light daily alcohol consumption lengthen life? In any observational study of this question, we expect drinking behavior-that is, treatment assignment-to be confounded by measured and unmeasured covariates. An observational study has two evidence factors if there are two essentially independent tests of the same null hypothesis about causal effects, where the two tests are susceptible to different unmeasured biases in treatment assignment. Because they are independent, two evidence factors...
-
作者：Harshaw, Christopher; Middleton, Joel; Savje, Fredrik
作者单位：Columbia University; Uppsala University; Uppsala University
摘要：Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome for any unit. The problem is exacerbated by interference and complex experimental designs. Experimenters must accept conservative variance estimators in these settings, but they can strive to minimize the conservativeness. In this article, we show that the task of constructing a minimally conservative variance estima...
-
作者：Kuang, Qi; Wang, Chao; Jiao, Yuling; Zhou, Fan
作者单位：Jiangxi University of Finance & Economics; Jiangxi University of Finance & Economics; Shanghai University of Finance & Economics; Wuhan University; Wuhan University
摘要：This article investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods, we aim to estimate the entire return distribution. To this end, we introduce a quantile-based approach for OPE using deep quantile process regression, presenting a novel algorithm called Deep Quantile Process regression-based Off-Policy Evaluation (DQPOPE). We provide new theoretical insights in...
-
作者：Franzolini, Beatrice; De Iorio, Maria; Eriksson, Johan
作者单位：Bocconi University; National University of Singapore; Agency for Science Technology & Research (A*STAR)
摘要：Standard clustering techniques assume a common clustering configuration for all features in a dataset. However, when dealing with multi-view or longitudinal data, the clusters' number, frequencies, and shapes may need to vary across features to accurately capture dependence structures and heterogeneity. In this setting, classical model-based clustering fails to account for within-subject dependence across domains. We introduce conditional partial exchangeability, a novel probabilistic paradigm...
-
作者：Ki, Dohyeong; Guntuboyina, Adityanand
作者单位：University of California System; University of California Berkeley
摘要：Shape constraints in nonparametric regression provide a powerful framework for estimating regression functions under realistic assumptions without tuning parameters. However, most existing methods-except additive models-impose too weak restrictions, often leading to overfitting in high dimensions. Conversely, additive models can be too rigid, failing to capture covariate interactions. This article introduces a novel multivariate shape-constrained regression approach based on total concavity, o...
-
作者：Wang, Jiayi; Shi, Chengchun; Qi, Zhengling
作者单位：University of Texas System; University of Texas Dallas; University of London; London School Economics & Political Science; George Washington University
摘要：As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this article, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach uses the observed action, either from AI or humans, as input for achieving a stronger oracle in policy learning for the decision ...
-
作者：Lin, Xihong
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
-
作者：Garcia-Portugues, Eduardo; Paindaveine, Davy; Verdebout, Thomas
作者单位：Universidad Carlos III de Madrid; Universite Libre de Bruxelles; Universite Libre de Bruxelles
摘要：We consider a broad class of symmetry hypothesis testing problems that includes the problems of testing uniformity or rotational symmetry on the hypersphere Sd-1, as well as the problem of testing sphericity in R-d. For this class, we study the null and non-null behaviors of Sobolev tests, with emphasis on their consistency rates and corresponding asymptotic powers. Our main results show that: (i) Sobolev tests exhibit a detection threshold that depends not only on the coefficients defining th...
-
作者：Agterberg, Joshua; Lubberts, Zachary; Arroyo, Jesus
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia; Texas A&M University System; Texas A&M University College Station
摘要：Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating information across...
