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Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding

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arXiv 2025年

作者： Hafer, Marlies Marx, Alexander Department of Statistics TU Dortmund University Germany Research Center Trustworthy Data Science and Security University Alliance Ruhr Germany

We consider the problem of estimating the expected causal effect E[Y |do(X)] for a target variable Y when treatment X is set by intervention, focusing on continuous random variables. In settings without selection bias or confounding, E[Y |do(X)] = E[Y |X], which can be estimated using standard regression methods. However, regression fails when systematic missingness induced by selection bias, or confounding distorts the data. Boeken et al. [2023] show that when training data is subject to selection, proxy variables unaffected by this process can, under certain constraints, be used to correct for selection bias to estimate E[Y |X], and hence E[Y |do(X)], reliably. When data is additionally affected by confounding, however, this equality is no longer valid. Building on these results, we consider a more general setting and propose a framework that incorporates both selection bias and confounding. Specifically, we derive theoretical conditions ensuring identifiability and recoverability of causal effects under access to external data and proxy variables. We further introduce a two-step regression estimator (TSR), capable of exploiting proxy variables to adjust for selection bias while accounting for confounding. We show that TSR coincides with prior work if confounding is absent, but achieves a lower variance. Extensive simulation studies validate TSR’s correctness for scenarios which may include both selection bias and confounding with proxy variables. Copyright © 2025, The Authors. All rights reserved.

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Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference 41

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Classification under Nuisance Parameters and Generalized Lab...

41st International Conference on Machine Learning, ICML 2024

作者： Masserano, Luca Shen, Alex Doro, Michele Dorigo, Tommaso Izbicki, Rafael Lee, Ann B. Department of Statistics and Data Science Carnegie Mellon University Pittsburgh United States Machine Learning Department Carnegie Mellon University Pittsburgh United States Department of Physics and Astronomy Università di Padova Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy Lulea Techniska Universitet Lulea Sweden Universal Scientific Education and Research Network Italy Department of Statistics Universidade Federal de São Carlos São Paulo Brazil

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of distributional shift as generalized label shift (GLS). Direct classification using observed data X as covariates leads to biased predictions and invalid uncertainty estimates of labels Y. We overcome these biases by proposing a new method for robust uncertainty quantification that casts classification as a hypothesis testing problem under nuisance parameters. The key idea is to estimate the classifier's receiver operating characteristic (ROC) across the entire nuisance parameter space, which allows us to devise cutoffs that are invariant under GLS. Our method effectively endows a pretrained classifier with domain adaptation capabilities and returns valid prediction sets while maintaining high power. We demonstrate its performance on two challenging scientific problems in biology and astroparticle physics with data from realistic mechanistic models. Copyright 2024 by the author(s)

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Symmetric KL-divergence by Stein's Method

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arXiv 2024年

作者： Yao, Liu-Quan Liu, Song-Hao Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Statistics and Data Science Southern University of Science and Technology Guangdong Shenzhen518055 China

In this paper, we consider the symmetric KL-divergence between the sum of independent variables and a Gaussian distribution, and obtain a convergence rate of order O (ln n √n). The proof is based on Stein’s method. The convergence rate of order O (1√n) and O (n1) are also obtained under higher moment *** Codes 60F05, 60G50 © 2024, CC BY.

关键词： Gaussian distribution

Combining Kibria-Lukman and principal component estimators for the distributed lag models

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Behaviormetrika 2023年第2期50卷 621-652页

作者： Lukman, A.F. Norouzirad, M. Marques, F.J. Mazarei, D. Department of Mathematics University of Medical Sciences Ondo Nigeria Center for Mathematics and Applications (NOVA Math) NOVA SST Caparica Portugal Center for Mathematics and Applications (NOVA Math) and Department of Mathematics NOVA SST Caparica Portugal Department of Statistics Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran

Researchers are likely to encounter the problem of multicollinearity in the distributed lag model due to its nature. To address multicollinearity, biased estimation techniques such as the Almon ridge estimator may be preferred over the Almon estimator. By integrating the principal component (PC) approach with the Almon Kibria-Lukman (KL) estimator, the Almon principal component Kibria-Lukman estimator is proposed in this paper. The new technique possesses the advantage of the principal component estimator and the Almon-KL estimator. Almon-PC-KL estimator dominates the other estimators considered in this study in terms of theoretical comparison and simulation. © 2023, The Behaviormetric Society.

关键词： Almon estimator Almon-PC-KL estimator Distributed lag model Principal component Ridge

A Constant Time Algorithm for Solving Simple Rolling Cube Mazes 34

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A Constant Time Algorithm for Solving Simple Rolling Cube Ma...

34th Canadian Conference on Computational Geometry, CCCG 2022

作者： Tuggle, Randal Murphy, Davis Lorch, Nicholas Department of Computer Science University of North Carolina Chapel Hill United States Department of Mathematics Berry College United States Department of Statistics University of Georgia United States

In a rolling cube maze, a cube is placed on a board and the task is to roll it to a desired final space. Many variations of this puzzle exist. In this paper, we establish formal notation regarding rolling cube mazes and solve a simple variant: find a shortest path that puts a desired label on top at the final space. Utilizing several symmetries and reductions, we then produce a description of the solution path in constant time. This provides a framework for future researchers to develop algorithms to efficiently solve more complex mazes. © 2022 Yeganeh Bahoo and Konstantinos Georgiou.

关键词： Geometry

Two-dimensional horizontal visibility graph analysis of human brain aging on gray matter

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Chinese Physics B 2023年第7期32卷 558-563页

作者：倪黄晶杜若瑜梁磊花玲玲朱丽华秦姣龙 School of Geographic and Biologic Information Nanjing University of Posts and TelecommunicationsNanjing 210003China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing 210003China Department of Psychiatry the Affiliated Brain Hospital of Nanjing Medical UniversityNanjing 210029China Jiangsu Health Vocational College Nanjing 211800China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain ***,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain *** this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices *** network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these *** results demonstrate a decrease in the NNSEs of gray matter with *** with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate *** addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral *** study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.

关键词： two-dimensional horizontal visibility graph brain aging structural magnetic resonance imaging network structure entropy

Diagnostic Framework for Mumps Based on Deep Learning with Enhanced Mayfly Optimization Algorithm 1

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Diagnostic Framework for Mumps Based on Deep Learning with E...

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mercy, S. Sudha Madhavi, S. Keerthika, A. Prathima, Ch Prabhu, R.T. R.M.K Engineering College Department of Computer Science & Design Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Department of It Chennai India Aarupadai Veedu Institute of Technology Department of Ece Chennai India Department of Data Science Tirupati India Saveetha School of Engineering Simats Department of Ece Chennai India

ISBN: (纸本)9798350316469

The mumps virus causes a very infectious sickness. The enlargement of the parotid salivary glands can be quite uncomfortable. Mumps treatment centers on symptom management. The illness must take its natural course. While most people experience very moderate symptoms, some develop severe consequences. The mumps virus can be avoided with the use of the MMR vaccination. In this research, we employ a convolutional neural network (CNN) technique to better automate mumps detection. In order to automate mumps detection, the authors of this research suggest a convolutional neural network (CNN) trained using XGBoost Classifier data. Incorporating machine learning (ML) technique comparisons. A mumps dataset consisting of 50×50 RGB picture patches served as the basis for all the models. Quantitative findings from each approach were put through validation testing based on their respective effectiveness metrics. To improve the efficiency of the model, the Mayfly Optimization Algorithm is implemented. Results show that the suggested method is effective, with 97% accuracy, suggesting that it might help decrease diagnostic errors caused by human error. And the suggested system we've developed is so precise that it outperforms the CNN model, which only gets around 92% of the time. Consequently, the suggested method is 5% more accurate than the CNN Model. © 2023 IEEE.

关键词： Convolutional Neural Network Mayfly Optimization Algorithm Mumps Classification XGBoost Classifier

Statistical inference for case-control logistic regression via integrating external summary data

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arXiv 2024年

作者： Shi, Hengchao Liu, Xinyi Zheng, Ming Yu, Wen Department of Statistics and Data Science School of Management Fudan University Shanghai200433 China School of Mathematical Science Fudan University Shanghai200433 China

Case-control sampling is a commonly used retrospective sampling design to alleviate imbalanced structure of binary data. When fitting the logistic regression model with case-control data, although the slope parameter of the model can be consistently estimated, the intercept parameter is not identifiable, and the marginal case proportion is not estimatable, either. We consider the situations in which besides the case-control data from the main study, called internal study, there also exists summary-level information from related external studies. An empirical likelihood based approach is proposed to make inference for the logistic model by incorporating the internal case-control data and external information. We show that the intercept parameter is identifiable with the help of external information, and then all the regression parameters as well as the marginal case proportion can be estimated consistently. The proposed method also accounts for the possible variability in external studies. The resultant estimators are shown to be asymptotically normally distributed. The asymptotic variance-covariance matrix can be consistently estimated by the case-control data. The optimal way to utilized external information is discussed. Simulation studies are conducted to verify the theoretical findings. A real data set is analyzed for illustration. © 2024, CC0.

关键词： Sampling

An Information-Theoretic Approach to Identifying Formulaic Clusters in Textual data

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arXiv 2025年

作者： Yoffe, Gideon Segev, Yair Sober, Barak Department of Statistics and Data Science The Hebrew University of Jerusalem Faculty of Theology Carl von Ossietzky Universität Oldenburg Germany

Texts, whether literary or historical, exhibit structural and stylistic patterns shaped by their purpose, authorship, and cultural context. Formulaic texts—characterized by repetition and constrained expression—tend to have lower variability in self-information compared to more dynamic compositions. Identifying such patterns in historical documents, particularly multi-author texts like the Hebrew Bible provides insights into their origins, purpose, and transmission. This study aims to identify formulaic clusters—sections exhibiting systematic repetition and structural constraints—by analyzing recurring phrases, syntactic structures, and stylistic markers. However, distinguishing formulaic from non-formulaic elements in an unsupervised manner presents a computational challenge, especially in high-dimensional and sample-poor data sets where patterns must be inferred without predefined labels. To address this, we develop an information-theoretic algorithm leveraging weighted self-information distributions to detect structured patterns in text. Our pproach directly models variations in sample-wise self-information to identify formulaicity. By extending classical discrete self-information measures with a continuous formulation based on differential self-information in multivariate Gaussian distributions, our method remains applicable across different types of textual representations, including neural embeddings under Gaussian priors. Applied to hypothesized authorial divisions in the Hebrew Bible, our approach successfully isolates stylistic layers, providing a quantitative framework for textual stratification. This method enhances our ability to analyze compositional patterns, offering deeper insights into the literary and cultural evolution of texts shaped by complex authorship and editorial processes. © 2025, CC BY.

关键词： Gaussian distribution