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A New Criterion for Confounder Selection

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  1. Persson, Emma & Häggström, Jenny & Waernbaum, Ingeborg & de Luna, Xavier, 2017. "Data-driven algorithms for dimension reduction in causal inference," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 280-292.
  2. Andreas Jensen & Per Kragh Andersen & John Sahl Andersen & Gorm Greisen & Lone Graff Stensballe, 2019. "Risk factors of post-discharge under-five mortality among Danish children 1997-2016: A register-based study," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-11, December.
  3. Anna Zaytseva & Pierre Verger & Bruno Ventelou, 2023. "Better together? A mediation analysis of general practitioners' performance in multi-professional group practice," Working Papers hal-04305092, HAL.
  4. Leonard Henckel & Emilija Perković & Marloes H. Maathuis, 2022. "Graphical criteria for efficient total effect estimation via adjustment in causal linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 579-599, April.
  5. Lefebvre, Geneviève & Atherton, Juli & Talbot, Denis, 2014. "The effect of the prior distribution in the Bayesian Adjustment for Confounding algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 227-240.
  6. Robert P Kosilek & Sebastian E Baumeister & Till Ittermann & Matthias Gründling & Frank M Brunkhorst & Stephan B Felix & Peter Abel & Sigrun Friesecke & Christian Apfelbacher & Magdalena Brandl & Konr, 2019. "The association of intensive care with utilization and costs of outpatient healthcare services and quality of life," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-15, September.
  7. Carolina Perez-Heydrich & Michael G. Hudgens & M. Elizabeth Halloran & John D. Clemens & Mohammad Ali & Michael E. Emch, 2014. "Assessing effects of cholera vaccination in the presence of interference," Biometrics, The International Biometric Society, vol. 70(3), pages 731-741, September.
  8. Hao, Meiling & Su, Pingfan & Hu, Liyuan & Szabo, Zoltan & Zhao, Qianyu & Shi, Chengchun, 2024. "Forward and backward state abstractions for off-policy evaluation," LSE Research Online Documents on Economics 124074, London School of Economics and Political Science, LSE Library.
  9. Contreras, Hugo Alejandro & Candia, Cristian & Olchevskaia, Rodrigo Vladislav Troncoso & Ferres, Leo & Celedón, María Loreto Bravo & Lepri, Bruno & Rodriguez-Sickert, Carlos, 2023. "Linking Physical Violence to Women's Mobility in Chile," SocArXiv uad59, Center for Open Science.
  10. repec:osf:socarx:uad59_v1 is not listed on IDEAS
  11. Rajgopal, Shivaram & White, Roger, 2019. "Cheating when in the hole: The case of New York city taxis," Accounting, Organizations and Society, Elsevier, vol. 79(C).
  12. Jie Gao & Haiyan Qu & Keith M. McGregor & Amrej Singh Yadav & Hon K. Yuen, 2022. "Associations between Duration of Homelessness and Cardiovascular Risk Factors: A Pilot Study," IJERPH, MDPI, vol. 19(22), pages 1-10, November.
  13. Thomas S. Richardson & James M. Robins & Linbo Wang, 2018. "Discussion of “Data†driven confounder selection via Markov and Bayesian networks†by Häggström," Biometrics, The International Biometric Society, vol. 74(2), pages 403-406, June.
  14. Edward H. Kennedy & Sivaraman Balakrishnan, 2018. "Discussion of “Data†driven confounder selection via Markov and Bayesian networks†by Jenny Häggström," Biometrics, The International Biometric Society, vol. 74(2), pages 399-402, June.
  15. Riccardo Vecchio & Eva Parga-Dans & Pablo Alonso González & Azzurra Annunziata, 2021. "Why consumers drink natural wine? Consumer perception and information about natural wine," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 9(1), pages 1-16, December.
  16. Matthew Cefalu & Francesca Dominici & Nils Arvold & Giovanni Parmigiani, 2017. "Model averaged double robust estimation," Biometrics, The International Biometric Society, vol. 73(2), pages 410-421, June.
  17. Yongnam Kim, 2019. "The Causal Structure of Suppressor Variables," Journal of Educational and Behavioral Statistics, , vol. 44(4), pages 367-389, August.
  18. David Bartram, 2021. "Cross-Sectional Model-Building for Research on Subjective Well-Being: Gaining Clarity on Control Variables," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 725-743, June.
  19. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2021. "Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison," Stats, MDPI, vol. 4(2), pages 1-21, June.
  20. Sara S. Nozadi & Li Li & Li Luo & Debra MacKenzie & Esther Erdei & Ruofei Du & Carolyn W. Roman & Joseph Hoover & Elena O’Donald & Courtney Burnette & Johnnye Lewis, 2021. "Prenatal Metal Exposures and Infants’ Developmental Outcomes in a Navajo Population," IJERPH, MDPI, vol. 19(1), pages 1-24, December.
  21. Karki, Suyen & Ryynänen, Olli-Pekka & Salokekkilä, Pirkko & Häggman-Laitila, Arja, 2023. "Bayesian analysis of the factors explaining the disruptive behaviour of care leavers: A retrospective document analysis," Children and Youth Services Review, Elsevier, vol. 155(C).
  22. Brandon Koch & David M. Vock & Julian Wolfson, 2018. "Covariate selection with group lasso and doubly robust estimation of causal effects," Biometrics, The International Biometric Society, vol. 74(1), pages 8-17, March.
  23. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
  24. Eric TC Lai & Ruby Yu & Jean Woo, 2020. "The Associations of Income, Education and Income Inequality and Subjective Well-Being among Elderly in Hong Kong—A Multilevel Analysis," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
  25. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
  26. Xun Lu, 2015. "A Covariate Selection Criterion for Estimation of Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 506-522, October.
  27. Sean Yiu & Li Su, 2018. "Covariate association eliminating weights: a unified weighting framework for causal effect estimation," Biometrika, Biometrika Trust, vol. 105(3), pages 709-722.
  28. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.
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