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Within-Person Variability Score-Based Causal Inference: A Two-Step Estimation for Joint Effects of Time-Varying

Satoshi Usami1

  • 1Department of Education, University of Tokyo, Tokyo, Japan. usami_s@p.u-tokyo.ac.jp.

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Summary
This summary is machine-generated.

This study introduces a new causal inference method to disentangle within-person changes from stable traits. The method accurately estimates treatment effects by controlling for individual differences using longitudinal data.

Keywords:
causal inferencelongitudinal datamarginal structural modelobservational studystructural nested mean model

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Area of Science:

  • Behavioral Science
  • Causal Inference
  • Longitudinal Data Analysis

Background:

  • Disentangling within-person variability from stable traits is crucial in behavioral science.
  • Existing methods may not adequately control for stable individual differences when analyzing time-varying treatments.

Purpose of the Study:

  • To propose a novel within-person variability score-based causal inference method.
  • To estimate joint effects of time-varying treatments while controlling for stable traits.
  • To address limitations of existing structural equation modeling (SEM) approaches.

Main Methods:

  • A two-step analysis involving calculating within-person variability scores via SEM.
  • Estimating causal parameters using potential outcome frameworks (MSMs or SNMMs).
  • Emphasizing SNMMs with G-estimation for robustness to model misspecification.

Main Results:

  • Simulations demonstrate the proposed method's ability to recover causal parameters accurately.
  • Failure to account for stable traits can lead to biased causal estimates.
  • The method was applied to sleep habits and mental health data from the Tokyo Teen Cohort study.

Conclusions:

  • The proposed method effectively disaggregates within-person effects from stable traits.
  • Accurate causal inference for time-varying treatments requires controlling for stable individual differences.
  • This approach offers a robust tool for analyzing complex longitudinal behavioral data.