Temporal Asymmetry in Cross-Lagged Panel Models

Improved Causal Inference from Longitudinal Data

Bibliographic Data

ID21641753
AuthorsMatthew Browne (0000-0001-7623-8179, Central Queensland University), Mitchell D Woodbright (0000-0002-4664-478X, Central Queensland University, corresponding author), Belinda Goodwin (University of Southern Queensland), Belinda C Goodwin (0000-0002-3425-4848, University of Southern Queensland), Alex Russell (0000-0002-3685-7220, Central Queensland University)
Year2026
Volume33
Issue2
Pages177-190
Publication date2026-03-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueStructural Equation Modeling: A Multidisciplinary Journal (JOURNAL)
Journal identifiersISSN: 1070-5511 • E-ISSN: 1532-8007
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10705511.2025.2569468
OpenAlexW4416663026
LanguageEN
References cited12

Inferring causal direction from longitudinal data remains challenging in the social sciences, especially without experimental control. Traditional methods like Granger causality and cross-lagged panel models (CLPMs) assess predictive relationships but cannot distinguish directional causal effects from reciprocal associations due to autoregression, latent confounding, or measurement error. We propose a structured, hypothesis-driven CLPM extension that tests temporal asymmetry through nested structural equation models. By formally comparing forward and reverse cross-lagged effects under constrained models, this method identifies asymmetric predictive influences, strengthening causal inference. Demonstrated through theoretical analysis, simulations, and real-world data, our approach maintains CLPM accessibility while providing a principled, statistically robust test for causal direction in observational time-series data

Asymmetry · Bayesian probability · Causal inference · Causal model · Inference · Longitudinal data · Economic Policies and Impacts · Intergenerational and Educational Inequality Studies · Spatial and Panel Data Analysis

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