Journal of the American Statistical Association

最新文章

• Reinforcement Learning with Continuous Actions Under Unmeasured Confounding AI翻译
  作者：Li, Yuhan; Han, Eugene; Hu, Yifan; Zhou, Wenzhuo; Qi, Zhengling; Cui, Yifan; Zhu, Ruoqing
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Irvine; George Washington University; Zhejiang University; Zhejiang University
  摘要：This article addresses the challenge of offline policy learning in continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we advance this field by establishing a novel identification result to enable the nonparametric estimation of policy value for a given target policy under an infinite-horizon framework. Leveraging this identi...
• Toward Interpretable Deep Generative Models via Causal Representation Learning AI翻译
  作者：Moran, Gemma; Aragam, Bryon
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Chicago
  摘要：Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods' surprising performance is due in part to their ability to learn implicit representations of complex, multi-modal data. Unfortunately, deep neural networks are notoriously black boxes that obscure these representations, making them difficult to interpret or analyze. To ...
• Efficiency of QMLE for Dynamic Panel Data Models with Interactive Effects AI翻译
  作者：Bai, Jushan
  作者单位：Columbia University
  摘要：This article studies the problem of efficient estimation of panel data models in the presence of an increasing number of incidental parameters. We formulate the dynamic panel as a simultaneous equations system, and derive the efficiency bound under the normality assumption. We then show that the Gaussian quasi-maximum likelihood estimator (QMLE) applied to the system achieves the efficiency bound without the normality assumption. Comparison of QMLE with the fixed effects approach is made. Supp...
• Tail Calibration of Probabilistic Forecasts AI翻译
  作者：Allen, Sam; Koh, Jonathan; Segers, Johan; Ziegel, Johanna
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Bern; KU Leuven; Universite Catholique Louvain
  摘要：Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we intr...
• Integrated Path Stability Selection AI翻译
  作者：Melikechi, Omar; Miller, Jeffrey W.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling false positive control in practice. However, stability selection often selects few features because existing bounds on E(FP) are relatively loose. In this article, we introduce a novel approach to stability selection based on integrating stability paths rather than maximizing over th...
• A Bayesian Criterion for Rerandomization AI翻译
  作者：Liu, Zhaoyang; Han, Tingxuan; Rubin, Donald B.; Deng, Ke
  作者单位：Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua University
  摘要：Rerandomization is a powerful tool for experiment-based causal inference because it can better balance covariates than classic randomized designs, thereby leading to more accurate causal effect estimation. However, basic rerandomization and some of its extensions do not prioritize covariates that believed to be strongly associated with potential outcomes. To address this limitation, and thereby create more efficient rerandomization procedures, the quantification of covariate heterogeneity is a...
• Partially Exchangeable Stochastic Block Models for (Node-Colored) Multilayer Networks AI翻译
  作者：Gaffi, Francesco; Durante, Daniele; Lijoi, Antonio; Prunster, Igor
  作者单位：Bocconi University; University System of Maryland; University of Maryland College Park
  摘要：Multilayer networks generalize single-layered connectivity data in several directions. These generalizations include, among others, settings where multiple types of edges are observed among the same set of nodes (edge-colored networks) or where a single notion of connectivity is measured between nodes belonging to different pre-specified layers (node-colored networks). While progress has been made in statistical modeling of edge-colored networks, principled approaches that flexibly account for...
• Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment AI翻译
  作者：Ben-Michael, Eli; Greiner, D. James; Imai, Kosuke; Jiang, Zhichao
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Harvard University; Harvard University; Harvard University; Sun Yat Sen University
  摘要：Algorithmic recommendations and decisions have become ubiquitous in today's society. Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability. We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. Our goal is to analyze data f...
• Network Varying Coefficient Model AI翻译
  作者：Fan, Xinyan; Fang, Kuangnan; Lan, Wei; Tsai, Chih-Ling
  作者单位：Renmin University of China; Renmin University of China; Xiamen University; Xiamen University; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; University of California System; University of California Davis
  摘要：We propose a novel network-varying coefficient model that extends traditional varying coefficient models to accommodate network data. The main idea is to model the regression coefficients as the functions of the latent locations of network nodes that drive formation of the network. To estimate the model, we identify the latent locations via the latent space model and then develop an iterative projected gradient descent algorithm by optimizing the network parameters and regression coefficients ...
• On the Comparative Analysis of Average Treatment Effects Estimation via Data Combination AI翻译
  作者：Wu, Peng; Luo, Shanshan; Geng, Zhi
  作者单位：Beijing Technology & Business University
  摘要：There is growing interest in exploring causal effects in target populations via data combination. However, most approaches are tailored to specific settings and lack comprehensive comparative analyses. In this article, we focus on a typical scenario involving a source dataset and a target dataset. We first design six settings under covariate shift and conduct a comparative analysis by deriving the semiparametric efficiency bounds for the ATE in the target population. We then extend this analys...