Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions
NCI - National Cancer Institute
About This Grant
PROJECT SUMMARY/ABSTRACT During the past decade, the tobacco product landscape has evolved rapidly with a remarkable decrease in cigarette smoking prevalence, a dramatic increase in the popularity of electronic cigarettes (e-cigarettes), and the emergence of other novel tobacco products such as heated tobacco products. E-cigarettes have been the most used tobacco products among US middle and high school students for several years. In response to this trend, the Surgeon General declared an epidemic of e-cigarette use among youth in 2018. The escalating use of e-cigarettes, particularly among adolescents and young adults, raises significant concerns about their nicotine exposure and potential health harm. Therefore, it is essential to understand the complex interplay of individual modifiable risk factors and the initiation of e-cigarette use to craft effective preventive strategies for reducing e-cigarette use among these young demographics. Survey data has traditionally been the cornerstone of tobacco regulatory research, offering valuable insights into behavior patterns, monitoring changes in tobacco usage, and evaluating the impact of regulatory measures. Yet, as more comprehensive tobacco-related datasets become available, innovative methods are needed to analyze this wealth of information and to extract deeper behavioral insights, forecast trends, and investigate the causes of these trends. Despite the proven value of causal machine learning in various fields, their use in tobacco control has been limited. Therefore, I plan to integrate this advanced approach with survey data to enhance the understanding and prediction of tobacco use behaviors and aid in designing optimal tobacco interventions. I will focus on understanding the uptake of e-cigarette use and studying causal pathways leading to this behavior among tobacco-naïve adolescents and tobacco-naïve young adults. To achieve this, I will need further training in machine learning, statistics, and youth e-cigarette use. As such, I will take a series of formal courses to fill in my knowledge gaps. In addition, I will work closely with my mentors and collaborators whose areas of expertise will help me to realize my training and research goals. This K01 proposal will provide me with the protected time to acquire the skills and training necessary to become a leading researcher specializing in the application of causal machine learning to address tobacco-related issues and develop policy assessment tools.
Grant Summary
Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions is a NCI - National Cancer Institute grant providing up to $159K for university, nonprofit, healthcare org. Applications are due 2031-04-30 (open). Check eligibility and apply with FindGrants.
Focus Areas
Eligibility
How to Apply
Up to $159K
2031-04-30
- 1Confirm your organization is eligible for Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions from NCI - National Cancer Institute, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NCI - National Cancer Institute before the deadline.
Don't want to draft it yourself?
We'll draft the complete application against NCI - National Cancer Institute's requirements, run a quality review, and email you a submission-ready PDF plus an editable Word doc within 5 business days. Most orders deliver in 24-48 hours. Flat $399, any grant size.
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.
Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions: Frequently Asked Questions
Who is eligible for the Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions?
Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions is offered by NCI - National Cancer Institute and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions provide?
Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions provides up to $159K per award from NCI - National Cancer Institute. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions deadline?
Applications for Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions are due 2031-04-30 (open). Because deadlines can change, verify the date with the funder, NCI - National Cancer Institute, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions?
To apply for Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NCI - National Cancer Institute.