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

潜在状态特质(LST)理论是研究心理特质变化过程的最有影响力的理论之一。LST模型使研究人员能够以灵活和多样的方式研究性状变化和变异过程。核心但尚未解决的问题涉及在什么条件下(LST模型中的参数)可以从观测数据中进行因果解释和估计。该项目从三个角度考虑LST模型中的因果推断问题：a)统计模型与因果模型，b)离散时间与连续时间模型，c)随机与基于图的因果理论(Steyer vs.PER)。该项目的贡献在于系统和全面地研究了在具有自回归效应的离散时间和连续时间潜在状态-特征模型中，如何定义和估计时变变量、时间稳定变量及其交互作用对特征变化和变异性参数的因果效应。除了理论上的发展，新开发的LST模型在模拟研究和实际数据应用中也得到了广泛的研究。

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.