Definition and estimation of causal effects in latent state trait models (CaST)

潜在状态特质模型 (CaST) 因果效应的定义和估计

• 批准号: 523691032
• 负责人: Professor Dr. Tobias Koch, Ph.D.
• 金额: --
• 依托单位: Institut für Psychologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/523691032?language=en
• 关键词: Definition; estimation; causal; effects; latent

Latent State Trait (LST) theory is one of the most influential theories for studying processes of change and variability in psychological traits. LST models allow researchers to study trait change and variability processes in a flexible and versatile way. Central but as yet unresolved questions concern the conditions under which (the parameters in) LST models can be causally interpreted and estimated from observational data. The project considers the issue of causal inference in LST models from three perspectives: a) statistical versus causal models, b) discrete-time versus continuous-time models), and c) stochastic versus graph-based (Steyer vs. Pearl) causal theory. The contribution of the project lies in a systematic and comprehensive investigation of how causal effects of time-varying variables, time-stable variables, and their interactions on trait changes and variability parameters can be defined and estimated in discrete-time and continuous-time latent state-trait models with autoregressive effects. In addition to the theoretical developments, the newly developed LST models are extensively investigated in simulation studies and real data applications.

潜在状态特质理论是研究心理特质变化和变异过程的最有影响力的理论之一。LST模型允许研究人员以灵活和通用的方式研究性状变化和变异过程。中心，但尚未解决的问题涉及的条件下，（参数）LST模型可以因果解释和估计的观测数据。该项目从三个角度考虑LST模型中的因果推理问题：a）统计与因果模型，B）离散时间与连续时间模型，以及c）随机与基于图形（Steyer与Pearl）的因果理论。该项目的贡献在于系统和全面地调查了时变变量，时间稳定变量及其相互作用对性状变化和变异性参数的因果效应如何在具有自回归效应的离散时间和连续时间潜在状态-性状模型中定义和估计。除了理论上的发展，新开发的LST模型在模拟研究和真实的数据应用中得到了广泛的研究。