Bayesian Propensity Score-Augmented Latent Factor Model (PS-LFM)

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A Bayesian framework that combines latent factor models to account for unobserved time-series cross-sectional confounding with propensity score incorporation (via stratification) in the outcome model to facilitate credible causal comparisons.

Why It Matters

This is the core methodological contribution, explicitly merging latent factor modeling for unobserved confounding with propensity score augmentation for causal inference in a Bayesian time-series context.

Related Papers

• openalex-2603.25010-bayesian-propensity-score-augmented-latent-factor-models-for