从观察数据中估计因果量对于理解医疗治疗的安全性和有效性至关重要。然而，为了做出可靠的推断，医疗从业者不仅需要估计平均因果量，如条件平均治疗效应，还需要理解治疗效应的随机性作为随机变量。这种随机性称为偶然不确定性，对于理解治疗的获益概率或治疗效应的分位数是必要的。然而，治疗效应的偶然不确定性在因果机器学习社区中却受到 surprisingly little attention。为填补这一空白，我们旨在量化治疗效应在协变量条件水平上的偶然不确定性，即治疗效应的条件分布（CDTE）。与平均因果量不同，CDTE在没有强额外假设的情况下不是点可识别的。为此，我们采用部分可识别性来获得CDTE的精确界限，从而量化治疗效应的偶然不确定性。我们然后开发了一种新的、正交的学习者用于CDTE的界限，称为AU学习者。我们进一步表明，我们的AU学习者有几个优势，即它满足Neyman正交性，因此具有准Oracle效率。最后，我们提出了一种完全参数化的深度学习实例化形式的AU学习者。
英文摘要：
Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical practitioners require not only estimating averaged causal quantities, such as the conditional average treatment effect, but also understanding the randomness of the treatment effect as a random variable. This randomness is referred to as aleatoric uncertainty and is necessary for understanding the probability of benefit from treatment or quantiles of the treatment effect. Yet, the aleatoric uncertainty of the treatment effect has received surprisingly little attention in the causal machine learning community. To fill this gap, we aim to quantify the aleatoric uncertainty of the treatment effect at the covariate-conditional level, namely, the conditional distribution of the treatment effect (CDTE). Unlike average causal quantities, the CDTE is not point identifiable without strong additional assumptions. As a remedy, we employ partial identification to obtain sharp bounds on the CDTE and thereby quantify the aleatoric uncertainty of the treatment effect. We then develop a novel, orthogonal learner for the bounds on the CDTE, which we call AU-learner. We further show that our AU-learner has several strengths in that it satisfies Neyman-orthogonality and, thus, quasi-oracle efficiency. Finally, we propose a fully-parametric deep learning instantiation of our AU-learner.
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
College of Aerospace Science and Engineering National University of Defense Technology（国防科技大学航空航天学院）；Defense Innovation Institute Chinese Academy of Military Science（中国人民解放军军事科学院国防创新研究院）
One step closer to unbiased aleatoric uncertainty estimation
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；IBM Research（IBM研究院）；University of California（加利福尼亚大学）
Heinz Nixdorf Institute（海因茨·尼克斯多夫研究所）；Paderborn University（帕德博恩大学）；UMR CNRS 7253 Heudiasyc（法国国家科学研究中心联合研究单位7253 Heudiasyc）；Sorbonne Universités（索邦大学）；Université de Technologie de Compiègne（贡比涅技术大学）
HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
Department of Earth, Environment, and Planetary Sciences, Brown University（布朗大学地球、环境与行星科学系）；Data Science Institute, Brown University（布朗大学数据科学研究院）；Department of Computer Science, Brown University（布朗大学计算机科学系）
What Uncertainties Do We Need for Dynamical Systems?
Institute of Computer Science, LMU Munich（慕尼黑大学计算机科学研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Mathematics, LMU Munich（慕尼黑大学数学系）；German Research Center for Artificial Intelligence (DFKI, DSA)（德国人工智能研究中心（DFKI, DSA））
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Northeastern University（东北大学）；risQ Inc.（risQ公司）；Bay Area Environmental Research Institute（湾区环境研究所）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；NASA Advanced Supercomputing Division（美国国家航空航天局高级超级计算部）
Projected Boosting with Fairness Constraints: Quantifying the Cost of Fair Training Distributions
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA（生物统计学系，范德比尔特大学医学中心）；Department of Informatics, King's College London, WC2R 2LS London, UK（信息学系，伦敦国王学院）
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航空航天学院）；Defense Innovation Institute, Chinese Academy of Military Science（中国人民解放军军事科学院国防创新研究院）
Uncertainty Estimation and Out-of-Distribution Detection for LiDAR Scene Semantic Segmentation
Institute of Cartography and Geoinformatics, Leibniz University Hannover（汉诺威莱布尼茨大学制图与地理信息研究所）；Institute of Photogrammetry and GeoInformation, Leibniz University Hannover（汉诺威莱布尼茨大学摄影测量与地理信息研究所）
Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling
Faculty of Mechanical Engineering Delft University of Technology（代尔夫特理工大学机械工程学院）；School of Engineering Brown University（布朗大学工程学院）
Uncertainty Quantification for Regression using Proper Scoring Rules
Department of Machine Learning, MBZUAI（机器学习部门，MBZUAI）；ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz（林兹ELLIS单元和LIT AI实验室，机器学习研究所，林兹约翰内斯·开普勒大学）
