Adaptive sparsening and smoothing of the treatment model for longitudinal causal inference using outcome-adaptive LASSO and marginal fused LASSO

TL;DR

This paper introduces a novel two-step variable selection method for longitudinal causal inference, combining outcome-adaptive LASSO and marginal fused LASSO to improve model efficiency and reduce bias in time-varying treatment settings.

Contribution

It develops a new approach for confounder selection and treatment model simplification in longitudinal data, with theoretical justification and practical implementation.

Findings

Simulation studies demonstrate improved estimator efficiency.

Method effectively reduces model misspecification bias.

Applied to adolescent alcohol initiation data, revealing causal effects.

Abstract

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.