Within-Person Variability Score-Based Causal Inference: A Two-Step Semiparametric Estimation for Joint Effects of Time-Varying Treatments
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Behavioral science researchers have recently shown strong interest in disaggregating within- and between-person effects (stable traits) from longitudinal data. In this paper, we propose a method of within-person variability score-based causal inference for estimating joint effects of time-varying continuous treatments by effectively controlling for stable traits as time-invariant unobserved confounders. After conceptualizing stable trait factors and within-person variability scores, we introduce the proposed method, which consists of a two-step analysis. Within-person variability scores for each person, which are disaggregated from stable traits of that person, are first calculated using weights based on a best linear correlation preserving predictor through structural equation modeling. Causal parameters are then estimated via a potential outcome approach, either marginal structural models (MSMs) or structural nested mean models (SNMMs), using calculated within-person variability scores. We emphasize the use of SNMMs with G-estimation because of its doubly robust property to model errors. Through simulation and empirical application to data regarding sleep habits and mental health status from the Tokyo Teen Cohort study, we show that the proposed method can recover causal parameters well and that causal estimates might be severely biased if one does not properly account for stable traits.
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