Many statistical models have been proposed to examine reciprocal cross-lagged causal effects from panel data. The present article aims to clarify how these various statistical models control for unmeasured time-invariant confounders, helping researchers understand the differences in the statistical models from a causal inference perspective. Assuming that the true data generation model (i.e., causal model) has time-invariant confounders that were not measured, we compared different statistical models (e.g., dynamic panel model and random-intercept cross-lagged panel model) in terms of the conditions under which they can provide a relatively accurate estimate of the target causal estimand. Based on the comparisons and realistic plausibility of these conditions, we made some practical suggestions for researchers to select a statistical model when they are interested in causal inference. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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清楚地思考交叉滞后面板模型中的时不变混杂因素：从因果推理角度选择统计模型的指南。


已经提出了许多统计模型来检查面板数据的相互交叉滞后因果效应。本文旨在阐明这些不同的统计模型如何控制不可测量的时不变混杂因素，帮助研究人员从因果推理的角度理解统计模型的差异。假设真实的数据生成模型（即因果模型）具有未测量的时不变混杂因素，我们在以下条件下比较了不同的统计模型（例如动态面板模型和随机截距交叉滞后面板模型）他们可以提供对目标因果估计的相对准确的估计。基于这些条件的比较和现实合理性，我们为研究人员在对因果推理感兴趣时选择统计模型提出了一些实用的建议。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。