IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v110y2023i4p1117-1124..html
   My bibliography  Save this article

Ancestor regression in linear structural equation models

Author

Listed:
  • C Schultheiss
  • P Bühlmann

Abstract

SummaryWe present a new method for causal discovery in linear structural equation models. We propose a simple technique based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally, this approach can then be extended to estimating the causal order among all variables. We provide explicit error control for false causal discovery, at least asymptotically. This holds true even under Gaussianity, where other methods fail due to non-identifiable structures. These Type I error guarantees come at the cost of reduced power. Additionally, we provide an asymptotically valid goodness-of-fit p-value for assessing whether multivariate data stem from a linear structural equation model.

Suggested Citation

  • C Schultheiss & P Bühlmann, 2023. "Ancestor regression in linear structural equation models," Biometrika, Biometrika Trust, vol. 110(4), pages 1117-1124.
    • Handle: RePEc:oup:biomet:v:110:y:2023:i:4:p:1117-1124.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asad008
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kalisch, Markus & Mächler, Martin & Colombo, Diego & Maathuis, Marloes H. & Bühlmann, Peter, 2012. "Causal Inference Using Graphical Models with the R Package pcalg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i11).
    2. J. Peters & P. Bühlmann, 2014. "Identifiability of Gaussian structural equation models with equal error variances," Biometrika, Biometrika Trust, vol. 101(1), pages 219-228.
    3. Peter Spirtes & Clark Glymour & Richard Scheines, 2001. "Causation, Prediction, and Search, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262194406, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bettendorf, Timo & Heinlein, Reinhold, 2019. "Connectedness between G10 currencies: Searching for the causal structure," Discussion Papers 06/2019, Deutsche Bundesbank.
    2. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    3. Bareinboim Elias & Pearl Judea, 2013. "A General Algorithm for Deciding Transportability of Experimental Results," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 107-134, June.
    4. Bouncken, Ricarda B. & Ratzmann, Martin & Kraus, Sascha, 2021. "Anti-aging: How innovation is shaped by firm age and mutual knowledge creation in an alliance," Journal of Business Research, Elsevier, vol. 137(C), pages 422-429.
    5. Fangting Zhou & Kejun He & Yang Ni, 2023. "Individualized causal discovery with latent trajectory embedded Bayesian networks," Biometrics, The International Biometric Society, vol. 79(4), pages 3191-3202, December.
    6. Maarten J. Bijlsma & Rhian M. Daniel & Fanny Janssen & Bianca L. De Stavola, 2017. "An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models," Demography, Springer;Population Association of America (PAA), vol. 54(2), pages 721-743, April.
    7. 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.
    8. Peter Bühlmann, 2013. "Causal statistical inference in high dimensions," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 77(3), pages 357-370, June.
    9. Aviral Kumar Tiwari & Micheal Kofi Boachie & Rangan Gupta, 2021. "Network Analysis of Economic and Financial Uncertainties in Advanced Economies: Evidence from Graph-Theory," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(1), pages 188-215, March.
    10. Vincenzina Vitale & Flaminia Musella & Paola Vicard & Valentina Guizzi, 2020. "Modelling an energy market with Bayesian networks for non-normal data," Computational Management Science, Springer, vol. 17(1), pages 47-64, January.
    11. Jenny Häggström, 2018. "Rejoinder to Discussions on: Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 407-410, June.
    12. Chen, Pu & Hsiao, Chih-Ying, 2008. "What happens to Japan if China catches a cold?: A causal analysis of Chinese growth and Japanese growth," Japan and the World Economy, Elsevier, vol. 20(4), pages 622-638, December.
    13. Chen, Pu & Chihying, Hsiao, 2007. "Learning Causal Relations in Multivariate Time Series Data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 1, pages 1-43.
    14. Rigana, Katerina & Wit, Ernst-Jan Camiel & Cook, Samantha, 2023. "A new way of measuring effects of financial crisis on contagion in currency markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    15. Jinyang Zheng & Zhengling Qi & Yifan Dou & Yong Tan, 2019. "How Mega Is the Mega? Exploring the Spillover Effects of WeChat Using Graphical Model," Information Systems Research, INFORMS, vol. 30(4), pages 1343-1362, December.
    16. Kaiyue Liu & Lihua Liu & Kaiming Xiao & Xuan Li & Hang Zhang & Yun Zhou & Hongbin Huang, 2024. "CL-NOTEARS: Continuous Optimization Algorithm Based on Curriculum Learning Framework," Mathematics, MDPI, vol. 12(17), pages 1-22, August.
    17. Hao Li & Jianjun Zhan & Haosen Wang & Zipeng Zhao, 2024. "A Novel Ensemble Method of Divide-and-Conquer Markov Boundary Discovery for Causal Feature Selection," Mathematics, MDPI, vol. 12(18), pages 1-21, September.
    18. Liu, Tianyu & Mukherjee, Somabha & Biswas, Rahul, 2024. "Tensor recovery in high-dimensional Ising models," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
    19. Ruijie Tang, 2024. "Trading with Time Series Causal Discovery: An Empirical Study," Papers 2408.15846, arXiv.org, revised Aug 2024.
    20. Flaminia Musella & Paola Vicard & Maria Chiara De Angelis, 2022. "A Bayesian Network Model for Supporting School Managers Decisions in the Pandemic Era," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(3), pages 1445-1465, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:biomet:v:110:y:2023:i:4:p:1117-1124.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.