Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal with uninformative signals due to position in the ranking, saliency, and other presentation factors. While it was recently shown how counterfactual learning-to-rank (LTR) approaches \cite{Joachims/etal/17a} can provably overcome presentation bias if observation propensities are known, it remains to show how to accurately estimate these propensities. In this paper, we propose the first method for producing consistent propensity estimates without manual relevance judgments, disruptive interventions, or restrictive relevance modeling assumptions. We merely require that we have implicit feedback data from multiple different ranking functions. Furthermore, we argue that our estimation technique applies to an extended class of Contextual Position-Based Propensity Models, where propensities not only depend on position but also on observable features of the query and document. Initial simulation studies confirm that the approach is scalable, accurate, and robust.
Designing and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials
Hasso Plattner Institute for Digital Engineering, University of Potsdam（波茨坦大学哈索·普拉特纳数字工程研究所）；Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山哈索·普拉特纳数字健康研究所）；Harvard University（哈佛大学）
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
Mederrata（梅德拉塔）；National Institutes of Health（美国国立卫生研究院）；University of Vermont（佛蒙特大学）
Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting
School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；Peng Cheng Laboratory（鹏城实验室）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shantou University（汕头大学）
Fair Visual Recognition via Intervention with Proxy Features
Beijing Jiaotong University（北京交通大学）
深度学习模型经常学习做出依赖于性别和种族等敏感社会属性的预测，这会带来显著的公平风险，特别是在社会应用中，例如，招聘银行业和刑事司法现有的工作解决这个问题，最大限度地减少社会属性的信息模型去偏置。然而，目标任务和社会属性之间的高度相关性使得偏见缓解与目标任务准确性不相容。回顾模型偏差的产生是因为关于偏差属性的特征的学习（即，偏误特征）有助于目标任务的优化，我们探讨了以下研究问题：\emph{Can we leverage proxy features to replace the role of bias feature in target task optimization for debiasing?}为此，我们提出了\emph{Proxy Debiasing}，首先将目标任务对偏误信息的学习从偏误特征转移到人工代理特征，然后在推理中采用因果干预消除代理特征。\emph{Proxy Debiasing}的核心思想是设计可控制的代理特征，一方面在训练阶段替代偏差特征对目标任务的贡献，另一方面在推理阶段易于通过干预去除。这保证了在不影响目标信息的情况下消除偏差特征，从而解决了先前去偏差解决方案中的公平性-准确性悖论。我们将\emph{Proxy Debiasing}应用于几个基准数据集，并在准确性和公平性方面取得了显着的改进。
Practical considerations when designing an online learning algorithm for an app-based mHealth intervention
Department of Biostatistics, University of Michigan（生物统计学系，密歇根大学）；University of Michigan（密歇根大学）；Medical School, University of Michigan（医学系，密歇根大学）；Frankel Cardiovascular Center, University of Michigan（弗兰克尔心血管中心，密歇根大学）；VA Ann Arbor Health System（安阿伯健康系统（VA））；College of Pharmacy, University of Michigan（药学院，密歇根大学）
Can Information Flows Suggest Targets for Interventions in Neural Circuits?
Allen Institute（艾伦研究所）；University of Washington（华盛顿大学）；JP Morgan Chase AI Research（摩根大通人工智能研究院）；Carnegie Mellon University（卡内基梅隆大学）；Neuroscience Institute（神经科学研究所）
When Names Change Verdicts: Intervention Consistency Reveals Systematic Bias in LLM Decision-Making
Indian Institute of Information Technology, Allahabad (IIITA)（印度阿勒颇理工学院，阿勒颇（IIITA））；National Institute of Electronics and Information Technology (NIELIT)（国家电子与信息技术研究所（NIELIT））
Model updating after interventions paradoxically introduces bias
University of Edinburgh（爱丁堡大学）；MRC Human Genetics Unit（医学研究委员会人类遗传学单元）；The Alan Turing Institute（阿兰·图灵研究所）；Durham University（杜伦大学）；Kings College Hospital（国王学院医院）；Wellcome Trust（惠康信托基金会）；University of Warwick（华威大学）；Warwick Mathematics Institute（华威数学研究所）
Optimizing Digital Therapeutic Interventions: Online Learning under Endogenous Adherence
Department of Industrial and Systems Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校工业与系统工程系）；College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）
