Often in public health, we are interested in the treatment effect of an intervention on a population that is systemically different from the experimental population the intervention was originally evaluated in. When treatment effect heterogeneity is present in a randomized controlled trial, generalizing the treatment effect from this experimental population to a target population of interest is a complex problem; it requires the characterization of both the treatment effect heterogeneity and the baseline covariate mismatch between the two populations. Despite the importance of this problem, the literature for variable selection in this context is limited. In this paper, we present a Group LASSO-based approach to variable selection in the context of treatment effect generalization, with an application to generalize the treatment effect of very low nicotine content cigarettes to the overall U.S. smoking population.

翻译：通常在公共卫生方面,我们关心干预对与实验人口系统不同的人口产生的治疗效果。当治疗效果的异质性出现在随机控制的试验中时,将实验人群的治疗效果推广到感兴趣的目标人群是一个复杂的问题;这要求描述两种人群之间的治疗效果异质性和基线共变不匹配。尽管这个问题很重要,但在这方面选择变量的文献是有限的。在本文中,我们介绍了在治疗效果普遍化方面基于LASSO的分组办法,在治疗效果普遍化方面选择变量,并应用将非常低的尼古丁烟含量的治疗效果普及到整个美国吸烟人口。