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Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions

Award Number: K01CA308939
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 05/01/2026
PERIOD OF PERFORMANCE END DATE: 04/30/2031

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Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions - 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $158,895 )
2026	2026	REGENTS OF THE UNIVERSITY OF MICHIGAN	1109 GEDDES AVE STE 3300	ANN ARBOR	MI	48109	WASHTENAW	USA	Cancer Research Manpower	000	1	4/17/2026	NEW	$158,895
														Subtotal = $158,895

Grand Total All Awards = $158,895

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Leveraging Causal Machine Learning Methods to Enhance Tobacco Control Interventions

Award Number: K01CA308939

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 05/01/2026

PERIOD OF PERFORMANCE END DATE: 04/30/2031

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