IDEAS home Printed from https://ideas.repec.org/p/ags/aaea25/360934.html

Vaping Vs. Smoking: The Links To Arthritis And Overall Health Using Double Machine Learning

Author

Listed:
  • Chike, Onyedikachi Emmanuel
  • Badruddoza, Syed
  • Lyford, Conrad

Abstract

No abstract is available for this item.

Suggested Citation

  • Chike, Onyedikachi Emmanuel & Badruddoza, Syed & Lyford, Conrad, 2025. "Vaping Vs. Smoking: The Links To Arthritis And Overall Health Using Double Machine Learning," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO 360934, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea25:360934
    DOI: 10.22004/ag.econ.360934
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/360934/files/75247_103618_105300_Chike_Poster_and_Cover_page.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.360934?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Christie Cherian & Eugenia Buta & Patricia Simon & Ralitza Gueorguieva & Suchitra Krishnan-Sarin, 2021. "Association of Vaping and Respiratory Health among Youth in the Population Assessment of Tobacco and Health (PATH) Study Wave 3," IJERPH, MDPI, vol. 18(15), pages 1-11, August.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    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. Leah A. Jacobs & Alec McClean & Zach Branson & Edward Kennedy & Alex Fixler, 2024. "Incremental Propensity Score Effects for Criminology: An Application Assessing the Relationship Between Homelessness, Behavioral Health Problems, and Recidivism," Journal of Quantitative Criminology, Springer, vol. 40(4), pages 707-726, December.
    2. Gozgor, Giray & Li, Jing & Saleem, Irfan & Shinwari, Riazullah, 2025. "The impact of women's political empowerment on renewable energy demand: Evidence from OECD countries," Energy Economics, Elsevier, vol. 141(C).
    3. Mao, Minghai & Raiola, Antonio & Yang, Da, 2025. "Double machine learning for Oaxaca-Blinder decomposition," Economics Letters, Elsevier, vol. 255(C).
    4. Asanov, Anastasiya-Mariya & Asanov, Igor & Buenstorf, Guido, 2024. "A low-cost digital first aid tool to reduce psychological distress in refugees: A multi-country randomized controlled trial of self-help online in the first months after the invasion of Ukraine," Social Science & Medicine, Elsevier, vol. 362(C).
    5. Justin Whitehouse & Qizhao Chen & Morgane Austern & Vasilis Syrgkanis, 2025. "Inference on Optimal Policy Values and Other Irregular Functionals via Softmax Smoothing," Papers 2507.11780, arXiv.org, revised Mar 2026.
    6. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    7. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Tang, Shengfang & Huang, Zhilin, 2022. "Empirical likelihood confidence interval for difference-in-differences estimator with panel data," Economics Letters, Elsevier, vol. 216(C).
    9. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
    10. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    11. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    12. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    13. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2026. "Estimating Causal Effects With Observational Data: Guidelines for Agricultural and Applied Economists," Journal of Agricultural Economics, Wiley Blackwell, vol. 77(2), pages 356-382, June.
    14. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    15. Anthony Strittmatter & Conny Wunsch, 2025. "Labor market sorting and the gender pay gap revisited," Journal of Population Economics, Springer;European Society for Population Economics, vol. 38(3), pages 1-41, September.
    16. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    17. Bingnan Guo & Yuren Qian & Xinyan Guo & Hao Zhang, 2025. "Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning," Sustainability, MDPI, vol. 17(11), pages 1-25, May.
    18. Myoung-jae Lee & Chirok Han, 2024. "Ordinary least squares and instrumental-variables estimators for any outcome and heterogeneity," Stata Journal, StataCorp LLC, vol. 24(1), pages 72-92, March.
    19. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    20. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).

    More about this item

    Keywords

    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ags:aaea25:360934. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    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.