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dynamite：用于动态多变量面板模型的 R 包AI 解读

dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising multiple measurements per multiple individuals measured in time. The package supports joint modeling of multiple response variables, time-varying and time-invariant effects, a wide range of discrete and continuous distributions, group-specific random effects, latent factors, and customization of prior distributions of the model parameters. Models in the package are defined via a…

用于纵向因果推断的反馈增强非齐次隐马尔可夫模型AI 解读

Hidden Markov models are widely used for modeling sequential data but typically have limited applicability in observational causal inference due to their strong conditional independence assumptions. I introduce feedback-augmented non-homogeneous hidden Markov model (FAN-HMM), which incorporate time-varying covariates and feedback mechanisms from past observations to latent states and future responses. Integrating these models with the structural causal model framework allows…

因果图中的聚类与结构稳健性AI 解读

Graphs are commonly used to represent and visualize causal relations. For a small number of variables, this approach provides a succinct and clear view of the scenario at hand. As the number of variables under study increases, the graphical approach may become impractical, and the clarity of the representation is lost. Clustering of variables is a natural way to reduce the size of the causal diagram, but it may erroneously change the essential properties of the causal…