Introduction: Logistic regression (LR)-type model limitations for causal inference are explained theoretically and empirically through the lens of the purported gateway effect from e-cigarette use to smoking. Previous studies have reported that baseline e-cigarette use quadruples odds of follow-up smoking (binarized) in LR-type models of adolescent longitudinal cohorts (LCs), such that increased e-cigarette use would counteract smoking declines. However, US population-level trends show accelerated smoking declines to record-lows when e-cigarette use increased, presenting an apparent paradox. Methods: Population Assessment of Tobacco and Health (USA) Youth Waves 3 to 4 were analyzed with Bayesian Additive Regression Trees (BART) to model baseline e-cigarette use (treatment) and change in number of days smoking from baseline to follow-up (numerical response) among never- and ever-smoking respondents (group effects), adjusting for confounding risk factors (socio-demographic, intra-individual, behavioural, peer influence, and family background). Unlike LR-type models, BART provides nonlinear, nonparametric modelling with counterfactuals and provides causal effect estimates with principled uncertainty estimation. Results: The average effect of e-cigarette use on smoking was both clinically and statistically significant among ever-smoking adolescents (-2 days smoking [diversionary effect; opposite to gateway]) and was not clinically significant among never-smoking adolescents (<1-day absolute change in days smoking [null effect]). Conclusions: When LC data are analyzed with causal inference techniques, the gateway effect disappears, consistent with population-level trends. This likely explains why gateway effects predicted in previous LR-type studies have not materialized in a population-level reversal/unexpected slowing of the US adolescent smoking decline, resolving the paradox.

翻译：引言：通过电子烟使用向吸烟转变的所谓“门户效应”视角，从理论和实证层面阐释了逻辑回归（LR）类模型在因果推断中的局限性。既往研究基于青少年纵向队列（LCs）的LR类模型报告，基线期电子烟使用使后续吸烟（二值化）的几率增加四倍，据此推断电子烟使用的增加将抵消吸烟率的下降。然而，美国人群层面的趋势显示，当电子烟使用增加时，青少年吸烟率下降速度加快至历史最低水平，呈现明显悖论。方法：采用贝叶斯加性回归树（BART）分析美国烟草与健康人口评估（美国）青年第3至4波数据，针对从未吸烟和曾经吸烟的受访者（分组效应），对基线期电子烟使用（处理变量）与从基线到随访期间吸烟天数的变化（数值响应变量）进行建模，并调整混杂风险因素（社会人口学、个体内部、行为、同伴影响及家庭背景）。与LR类模型不同，BART提供基于反事实的非线性非参数建模，并给出附带原理性不确定性估计的因果效应估计值。结果：在曾经吸烟的青少年中，电子烟使用对吸烟的平均效应具有临床和统计显著性（吸烟天数减少2天，呈现转移效应而非门户效应）；在从未吸烟的青少年中，该效应无临床显著性（吸烟天数绝对值变化<1天，视为无效效应）。结论：当采用因果推断技术分析LC数据时，“门户效应”消失，这与人群层面趋势一致。这很可能解释了为何既往LR类研究预测的门户效应未在人群层面出现（美国青少年吸烟率下降未逆转或意外放缓），从而解决了这一悖论。