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Готові списки джерел за темами / Spurious correlations

Добірка наукової літератури з теми "Spurious correlations"

Автор: Grafiati

Опубліковано: 8 червня 2024

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Статті в журналах з теми "Spurious correlations":

1

Ben-Zeev, Talia, and Jon R. Star. "Spurious Correlations in Mathematical Thinking." Cognition and Instruction 19, no. 3 (September 2001): 253–75. http://dx.doi.org/10.1207/s1532690xci1903_1.

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2

Ward, Andrew. "“Spurious Correlations and Causal Inferences”." Erkenntnis 78, no. 3 (November 11, 2012): 699–712. http://dx.doi.org/10.1007/s10670-012-9411-6.

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3

Jackson, D. A., and K. M. Somers. "The spectre of ?spurious? correlations." Oecologia 86, no. 1 (March 1991): 147–51. http://dx.doi.org/10.1007/bf00317404.

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4

Halperin, Silas. "Spurious correlations—causes and cures." Psychoneuroendocrinology 11, no. 1 (January 1986): 3–13. http://dx.doi.org/10.1016/0306-4530(86)90028-4.

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5

Lorenzo-Arribas, Altea, Penny S. Reynolds, and Chaitra H. Nagaraja. "Suffrage, Statistics, and Spurious Correlations." CHANCE 36, no. 4 (October 2, 2023): 51–54. http://dx.doi.org/10.1080/09332480.2023.2290956.

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6

Fan, Jianqing, Qi-Man Shao, and Wen-Xin Zhou. "Are discoveries spurious? Distributions of maximum spurious correlations and their applications." Annals of Statistics 46, no. 3 (June 2018): 989–1017. http://dx.doi.org/10.1214/17-aos1575.

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7

Berges, John A. "Ratios, regression statistics, and “spurious” correlations." Limnology and Oceanography 42, no. 5 (July 1997): 1006–7. http://dx.doi.org/10.4319/lo.1997.42.5.1006.

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8

Pollman, Curtis D., and Donald M. Axelrad. "Mercury bioaccumulation factors and spurious correlations." Science of The Total Environment 496 (October 2014): vi—xii. http://dx.doi.org/10.1016/j.scitotenv.2014.07.050.

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9

Richman, Jesse T., and Ryan J. Roberts. "Assessing Spurious Correlations in Big Search Data." Forecasting 5, no. 1 (February 28, 2023): 285–96. http://dx.doi.org/10.3390/forecast5010015.

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Анотація:

Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random walk distributions. Quantifying these spurious correlations and their likely magnitude for various distributions has value for several reasons. First, analysts can make progress toward accurate inference. Second, they can avoid unwarranted credulity. Third, they can demand appropriate disclosure from the study authors.

10

Sorjonen, Kimmo, Gustav Nilsonne, Michael Ingre, and Bo Melin. "Spurious correlations in research on ability tilt." Personality and Individual Differences 185 (February 2022): 111268. http://dx.doi.org/10.1016/j.paid.2021.111268.

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Більше джерел

Дисертації з теми "Spurious correlations":

1

Dediu, Dan. "Non-Spurious Correlations Between Genetic and Linguistic Diversities in the Context of Human Evolution." Thesis, University of Edinburgh, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490090.

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2

Tan, David Tatwei Banking &amp Finance Australian School of Business UNSW. "Corporate governance and firm outcomes: causation or spurious correlation?" Awarded By:University of New South Wales. Banking & Finance, 2009. http://handle.unsw.edu.au/1959.4/43371.

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The rapid growth of financial markets and the increasing diffusion of corporate ownership have placed tremendous emphasis on the effectiveness of corporate governance in resolving agency conflicts within the firm. This study investigates the corporate governance and firm performance/failure relation by implementing various econometric modelling methods to disaggregate causal relations and spurious correlations. Using a panel dataset of Australian firms, a comprehensive suite of corporate governance mechanisms are considered; including the ownership, remuneration, and board structures of the firm. Initial ordinary least squares (OLS) and fixed-effects panel specifications report significant causal relations between various corporate governance measures and firm outcomes. However, the dynamic generalised method of moments (GMM) results indicate that no causal relations exist when taking into account the effects of simultaneity, dynamic endogeneity, and unobservable heterogeneity. Moreover, these results remain robust when accounting for the firm??s propensity for fraud. The findings support the equilibrium theory of corporate governance and the firm, suggesting that a firm??s corporate governance structure is an endogenous characteristic determined by other firm factors; and that any observed relations between governance and firm outcomes are spurious in nature. Chapter 2 examines the corporate governance and firm performance relation. Using a comprehensive suite of corporate governance measures, this chapter finds no evidence of a causal relation between corporate governance and firm performance when accounting for the biases introduced by simultaneity, dynamic endogeneity, and unobservable heterogeneity. This result is consistent across all firm performance measures. Chapter 3 explores the corporate governance and likelihood of firm failure relation by implementing the Merton (1974) model of firm-valuation. Similarly, no significant causal relations between a firm??s corporate governance structure and its likelihood of failure are detected when accounting for the influence of endogeneity on the parameter estimates. Chapter 4 re-examines the corporate governance and firm performance/failure relation within the context of corporate fraud. Using KPMG and ASIC fraud databases, the corporate governance and firm outcome relations are estimated whilst accounting for the firms?? vulnerability to corporate fraud. This chapter finds no evidence of a causal relation between corporate governance and firm outcomes when conditioning on a firm??s propensity for fraud.

3

Bose, Tulika. "Transfer learning for abusive language detection." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0019.

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La prolifération des médias sociaux, malgré ses nombreux avantages, a entraîné une augmentation des propos injurieux. Ces propos, qui sont généralement blessants, toxiques ou empreints de préjugés à l'encontre d'individus ou de groupes, doivent être détectés et modérés rapidement par les plateformes en ligne. Les modèles d'apprentissage profond pour la détection de propos abusifs ont montré des niveaux de performance élevé quand ils sont évalués sur des données similaires à celles qui ont servi à entraîner les modèles, mais sont nettement moins performants s'ils sont évalués sur des données dont la distribution est différente. En outre, ils nécessitent une quantité considérable de données étiquetées coûteuses pour l'apprentissage. C'est pour cela qu'il est intéressant d'étudier le transfert efficace de connaissances à partir de corpus annotés existants de propos abusifs. Cette thèse étudie le problème de l'apprentissage par transfert pour la détection de propos abusifs et explore diverses solutions pour améliorer le transfert de connaissances dans des scénarios inter corpus.Tout d'abord, nous analysons la généralisabilité inter-corpus des modules de détection de propos abusifs sans accéder à des données cibles pendant le processus d'apprentissage. Nous examinons si la combinaison des représentations issues du thème (topic) avec des représentations contextuelles peut améliorer la généralisabilité. Nous montrons que l'association de commentaires du corpus cible avec des thèmes du corpus d'entraînement peut fournir des informations complémentaires pour un meilleur transfert inter-corpus.Ensuite, nous explorons l'adaptation au domaine non supervisée (UDA, Unsupervised Domain Adaptation), un type d'apprentissage par transfert transductif, avec accès au corpus cible non étiqueté. Nous explorons certaines approches UDA populaires dans la classification des sentiments pour la détection de propos abusifs dans le cadre de corpus croisés. Nous adaptons ensuite une variante du modèle BERT au corpus cible non étiqueté en utilisant la technique du modèle de langue avec masques (MLM Masked Language Model). Alors que cette dernière améliore les performances inter-corpus, les autres approches UDA ont des performances sous-optimales. Notre analyse révèle leurs limites et souligne le besoin de méthodes d'adaptation efficaces pour cette tâche.Comme troisième contribution, nous proposons deux approches d'adaptation au domaine utilisant les attributions de caractéristiques (feature attributions), qui sont des explications a posteriori du modèle. En particulier, nous étudions le problème des corrélations erronées (spurious correlations) spécifiques à un corpus qui limitent la généralisation pour la détection des discours de haine, un sous-ensemble des propos abusifs. Alors que les approches de la littérature reposent sur une liste de termes établie manuellement, nous extrayons et pénalisons automatiquement les termes qui causent des corrélations erronées. Nos approches dynamiques améliorent les performances dans le cas de corpus croisés par rapport aux travaux précédents, à la fois indépendamment et en combinaison avec des dictionnaires prédéfinis.Enfin, nous considérons le transfert de connaissances d'un domaine source avec beaucoup de données étiquetées vers un domaine cible, où peu d'instances étiquetées sont disponibles. Nous proposons une nouvelle stratégie d'apprentissage, qui permet une modélisation flexible de la proximité relative des voisins récupérés dans le corpus source pour apprendre la quantité de transfert utile. Nous incorporons les informations de voisinage avec une méthode de transport optimal (Optimal Transport ) qui exploite la géométrie de l'espace de représentation (embedding space) . En alignant les distributions conjointes de l'embedding et des étiquettes du voisinage, nous montrons des améliorations substantielles dans des corpus de discours haineux de taille réduite
The proliferation of social media, despite its multitude of benefits, has led to the increased spread of abusive language. Such language, being typically hurtful, toxic, or prejudiced against individuals or groups, requires timely detection and moderation by online platforms. Deep learning models for detecting abusive language have displayed great levels of in-corpus performance but underperform substantially outside the training distribution. Moreover, they require a considerable amount of expensive labeled data for training.This strongly encourages the effective transfer of knowledge from the existing annotated abusive language resources that may have different distributions to low-resource corpora. This thesis studies the problem of transfer learning for abusive language detection and explores various solutions to improve knowledge transfer in cross-corpus scenarios.First, we analyze the cross-corpus generalizability of abusive language detection models without accessing the target during training. We investigate if combining topic model representations with contextual representations can improve generalizability. The association of unseen target comments with abusive language topics in the training corpus is shown to provide complementary information for a better cross-corpus transfer.Secondly, we explore Unsupervised Domain Adaptation (UDA), a type of transductive transfer learning, with access to the unlabeled target corpus. Some popular UDA approaches from sentiment classification are analyzed for cross-corpus abusive language detection. We further adapt a BERT model variant to the unlabeled target using the Masked Language Model (MLM) objective. While the latter improves the cross-corpus performance, the other UDA methods perform sub-optimally. Our analysis reveals their limitations and emphasizes the need for effective adaptation methods suited to this task.As our third contribution, we propose two DA approaches using feature attributions, which are post-hoc model explanations. Particularly, the problem of spurious corpus-specific correlations is studied that restrict the generalizability of classifiers for detecting hate speech, a sub-category of abusive language. While the previous approaches rely on a manually curated list of terms, we automatically extract and penalize the terms causing spurious correlations. Our dynamic approaches improve the cross-corpus performanceover previous works both independently and in combination with pre-defined dictionaries.Finally, we consider transferring knowledge from a resource-rich source to a low-resource target with fewer labeled instances, across different online platforms. A novel training strategy is proposed, which allows flexible modeling of the relative proximity of neighbors retrieved from the resource-rich corpus to learn the amount of transfer. We incorporate neighborhood information with Optimal Transport that permits exploitingthe embedding space geometry. By aligning the joint embedding and label distributions of neighbors, substantial improvements are obtained in low-resource hate speech corpora

Книги з теми "Spurious correlations":

1

Vigen, Tyler. Spurious Correlations. Hachette Books, 2015.

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2

Vigen, Tyler. Spurious Correlations. Hachette Books, 2015.

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Частини книг з теми "Spurious correlations":

1

Evensen, Geir. "Spurious correlations, localization, and inflation." In Data Assimilation, 237–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03711-5_15.

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2

Hagos, Misgina Tsighe, Kathleen M. Curran, and Brian Mac Namee. "Unlearning Spurious Correlations in Chest X-Ray Classification." In Discovery Science, 387–97. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_26.

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3

Kumar, Amar, Nima Fathi, Raghav Mehta, Brennan Nichyporuk, Jean-Pierre R. Falet, Sotirios Tsaftaris, and Tal Arbel. "Debiasing Counterfactuals in the Presence of Spurious Correlations." In Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging, 276–86. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45249-9_27.

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4

Bykov, Kirill, Laura Kopf, and Marina M. C. Höhne. "Finding Spurious Correlations with Function-Semantic Contrast Analysis." In Communications in Computer and Information Science, 549–72. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44067-0_28.

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5

Wu, Jiaying, and Bryan Hooi. "Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks." In Machine Learning and Knowledge Discovery in Databases, 274–90. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_17.

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6

Negri, Fedra. "Correlation Is Not Causation, Yet… Matching and Weighting for Better Counterfactuals." In Texts in Quantitative Political Analysis, 71–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12982-7_4.

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AbstractAnyone who has attended a statistics class has heard the old adage “correlation does not imply causation,” usually followed by a series of hilarious graphs showing spurious correlations. Even if we strongly agree with it, this reminder has been taken a little too far: it is repeated like a mantra to criticize every observational study as being unable to detect causation behind statistical association. This chapter helps the reader go beyond the mantra, firstly, by explaining that “correlation does not imply causation” in observational studies because of selection bias (i.e. the composition of treatment and control groups follows a non-random selection) and parametric model dependence. Then, it introduces readers to weighting and matching techniques, smart statistical tools for reducing imbalance in the empirical distribution of pretreatment covariates between the treatment and control groups. Lastly, it provides an empirical illustration by focusing on two powerful algorithms: the entropy balancing (EB) and the coarsened exact matching (CEM). The chapter ends with caveats.

7

Sun, Susu, Lisa M. Koch, and Christian F. Baumgartner. "Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?" In Lecture Notes in Computer Science, 425–34. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43895-0_40.

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8

Sheather, Simon J. "Spurious Correlation." In International Encyclopedia of Statistical Science, 1374–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_534.

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9

Elío, Javier, Marina Georgati, Henning S. Hansen, and Carsten Keßler. "Migration Studies with a Compositional Data Approach: A Case Study of Population Structure in the Capital Region of Denmark." In Computational Science and Its Applications – ICCSA 2022 Workshops, 576–93. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10545-6_39.

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AbstractComputing percentages or proportions for removing the influence of population density has recently gained popularity, as it offers a deep insight into compositional variability. However, data are constrained to a constant sum and therefore are not independent observations, a fundamental limitation for applying standard multivariate statistical tools. Compositional Data (CoDa) techniques address the issue of standard statistical tools being insufficient for the analysis of closed data (i.e., spurious correlations, predictions outside the range, and sub-compositional incoherence) but they are not widely used in the field of population geography. Hence, in this article, we present a case study where we analyse at parish level the spatial distribution of Danes, Western migrants and non-Western migrants in the Capital region of Denmark. By applying CoDa techniques, we have been able to identify the spatial population segregation in the area and we have recognised patterns in the distribution of various demographic groups that can be used for interpreting housing prices variations. Our exercise is a basic example of the potentials of CoDa techniques which generate more robust and reliable results than standard statistical procedures in order to interpret the relations among various demographic groups. It can be further generalised to other population datasets with more complex structures.

10

Loehle, Craig. "Forest decline: endogenous dynamics, tree defenses, and the elimination of spurious correlation." In Temporal and Spatial Patterns of Vegetation Dynamics, 65–78. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2275-4_8.

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Тези доповідей конференцій з теми "Spurious correlations":

1

Thorn Jakobsen, Terne Sasha, Maria Barrett, and Anders Søgaard. "Spurious Correlations in Cross-Topic Argument Mining." In Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.starsem-1.25.

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2

Wang, Zhao, and Aron Culotta. "Identifying Spurious Correlations for Robust Text Classification." In Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.308.

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3

Yaghoobzadeh, Yadollah, Soroush Mehri, Remi Tachet des Combes, T. J. Hazen, and Alessandro Sordoni. "Increasing Robustness to Spurious Correlations using Forgettable Examples." In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.eacl-main.291.

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4

Ross, Alexis, Matthew Peters, and Ana Marasovic. "Does Self-Rationalization Improve Robustness to Spurious Correlations?" In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.emnlp-main.501.

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5

Kim, Jae Myung, A. Sophia Koepke, Cordelia Schmid, and Zeynep Akata. "Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00257.

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Durmus, Esin, Faisal Ladhak, and Tatsunori Hashimoto. "Spurious Correlations in Reference-Free Evaluation of Text Generation." In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.102.

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Gu, Jiatao, Yong Wang, Kyunghyun Cho, and Victor O. K. Li. "Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1121.

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Mu, Shanlei, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding, and Ji-Rong Wen. "Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3477495.3531934.

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Bansal, Parikshit, and Amit Sharma. "Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.127.

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10

Eisenstein, Jacob. "Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language." In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.naacl-main.321.

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