Causal variable selection in time-varying treatment settings is challenging due to evolving confounding effects. Existing methods mainly focus on time-fixed exposures and are not directly applicable to time-varying scenarios. We propose a novel two-step procedure for variable selection when modeling the treatment probability at each time point. We first introduce a novel approach to longitudinal confounder selection using a Longitudinal Outcome Adaptive LASSO (LOAL) that will data-adaptively select covariates with theoretical justification of variance reduction of the estimator of the causal effect. We then propose an Adaptive Fused LASSO that can collapse treatment model parameters over time points with the goal of simplifying the models in order to improve the efficiency of the estimator while minimizing model misspecification bias compared with naive pooled logistic regression models. Our simulation studies highlight the need for and usefulness of the proposed approach in practice. We implemented our method on data from the Nicotine Dependence in Teens study to estimate the effect of the timing of alcohol initiation during adolescence on depressive symptoms in early adulthood.

翻译：时变治疗设定中的因果变量选择因混杂效应的动态演变而面临挑战。现有方法主要针对时间固定暴露场景，无法直接适用于时变情形。本文提出一种新颖的两步法，用于对各时间点治疗概率建模时的变量选择。我们首先引入一种纵向混杂因子选择新方法——纵向结果自适应LASSO（LOAL），该方法能基于数据自适应地选择协变量，并从理论上证明其可降低因果效应估计量的方差。随后提出自适应融合LASSO，能够跨时间点合并治疗模型参数，旨在简化模型结构，相较于朴素合并逻辑回归模型，在提升估计量效率的同时最小化模型设定偏误。模拟研究验证了所提方法在实际应用中的必要性与有效性。我们将该方法应用于青少年尼古丁依赖研究数据，以估计青春期酒精开始饮用时机对成年早期抑郁症状的影响。