# Using Causal Machine Learning Methods to Inform Tobacco Regulatory Science

> **NIH NIH K01** · NEW YORK UNIVERSITY · 2024 · $177,877

## Abstract

PROJCT SUMMARY/ABSTRACT
Inconsistent findings regarding whether and how E-cigarette (EC) use influences subsequent tobacco use
behaviors complicate evidence-based tobacco regulation. Among youth, EC use is associated with greater risk
of transitioning to combustible cigarette (CC) smoking, but estimated effect sizes of EC exposure vary
substantially across studies. Among adults who currently smoke CCs, ECs show potential to help quit CC
smoking in some studies but not in others. These inconsistent findings may be due in part to a preponderance
of observational studies, use of small size cross-sectional data, and inadequate control for covariates, Further,
despite considerable heterogeneity in the size of estimated EC exposure effects, whether specific
characteristics modify the EC exposure effects has been largely ignored in literature. Understanding how ECs
influence subsequent CC smoking, particularly among vulnerable subgroups (e.g., age, gender), and their
intersectionality, will help inform regulatory activities that address tobacco-related health disparities. Lastly, it is
unclear whether estimated EC exposure effects from a certain population subgroup or at a certain time can be
generalized to different subgroups or times. Generalizable EC exposure effects could provide critical evidence
for tobacco regulators. To address these knowledge gaps, this study aims to use causal machine learning
methods to determine the influence of ECs on subsequent CC smoking, in overall US youth and adult
populations and in vulnerable subgroups, and to explore methods for estimating generalizable EC exposure
effects. A secondary analysis of the longitudinal Population Assessment of Tobacco and Health study will be
conducted to address the following specific aims. Aim 1: Determine average exposure effects of EC use on
subsequent CC smoking in youth and adults. Aim 2: Determine heterogeneous EC exposure effects among
vulnerable subgroups (age, gender, poverty, race/ethnicity). Aim 3: Evaluate the performance of causal
machine learning methods to generalize EC exposure effects using both simulated and PATH Study data. To
successfully accomplish these aims and develop into an independent methodologist in tobacco regulator
science (TRS), I will obtain training in the following areas: 1) TRS theories and measures, especially health
disparities in TRS; 2) Causal inference methods for evaluating exposure effects; and 3) Machine learning skills
for high-dimensional data analysis. During the award period, I will be supported in my research and training
goals by my institution and interdisciplinary mentoring team, which consists of experts in the fields of TRS,
causal inference, machine learning, and health disparities. The K01 research and training experience will result
in an R01 with the overarching goal of extending causal machine learning methods for generalization to
address more complex real-world questions. In the long term, I will bring TRS, machine learning me...

## Key facts

- **NIH application ID:** 10834209
- **Project number:** 5K01DA058408-02
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Shu Xu
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $177,877
- **Award type:** 5
- **Project period:** 2023-05-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10834209

## Citation

> US National Institutes of Health, RePORTER application 10834209, Using Causal Machine Learning Methods to Inform Tobacco Regulatory Science (5K01DA058408-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10834209. Licensed CC0.

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