Three assumptions for causal interpretation of cross-lagged panel models: consistency, strong ignorability, and positivity

Research Method · All research methods
Evidence · 1 claim (1 mixed) · 1 study (1 review), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description

Drawing on the Rubin causal model and potential outcomes, the article specifies the assumptions under which cross-lagged coefficients may be read causally. It states that "Usami et al. (2019a) outlined three assumptions that need to be satisfied in order for CLPMs to identify causal effects: (a) consistency, (b) strong ignorability/no unobserved confounding, and (c) positivity". Consistency ties observed outcomes to potential outcomes, strong ignorability requires all confounders to be measured, and positivity requires covariate overlap across treatment values. The article argues these are often unrealistic in REM research.

Accounts

• Three assumptions for causal interpretation of cross-lagged panel models: consistency, strong ignorability, and positivity: Drawing on the Rubin causal model and potential outcomes, the article specifies the assumptions under which cross-lagged coefficients may be read causally. It states that "Usami et al. (2019a) outlined three assumptions that need to be satisfied in order for CLPMs to identify causal effects: (a) consistency, (b) strong ignorability/no unobserved confounding, and (c) positivity". Consistency ties observed outcomes to potential outcomes, strong ignorability requires all confounders to be measured, and positivity requires covariate overlap across treatment values. The article argues these are often unrealistic in REM research. (Hübner et al. (2023))

Claims

• Simulation evidence shows RI-CLPM estimates are biased when the true model is a CLPM with lag-2 effects, and fit statistics cannot decide between the two [~W]

Related Research Methods

• Three structural causal models of the self-concept–achievement reciprocal relationship

Key Sources

• Hübner, N., Wagner, W., Zitzmann, S., & Nagengast, B. (2023). How Strong Is the Evidence for a Causal Reciprocal Effect? Contrasting Traditional and New Methods to Investigate the Reciprocal Effects Model of Self-Concept and Achievement. Educational Psychology Review. https://doi.org/10.1007/s10648-023-09724-6

Page metadata

Field	Value
type	research-method
id	three-assumptions-for-causal-interpretation-of-cross-lagged-panel-models-consistency-stron
title	Three assumptions for causal interpretation of cross-lagged panel models: consistency, strong ignorability, and positivity
description	Drawing on the Rubin causal model and potential outcomes, the article specifies the assumptions under which cross-lagged coefficients may be read causally.
status	review
generated	by process:settle-candidates on 2026-10-19
trust	unverified — no one has confirmed this page's content

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