We propose a Bayesian propensity score-augmented latent factor model for causal inference with time-series cross-sectional data. The framework explicitly models the treatment assignment mechanism by incorporating latent factor loadings, while the outcome model flexibly incorporates the propensity score, for example through stratification. Relative to existing approaches, the proposed method provides greater flexibility and captures additional heterogeneity across propensity-score strata, enabling more credible comparisons between treated and control units within each stratum. For estimation and inference, we adopt an approximate Bayesian procedure to address the model feedback problem common in Bayesian propensity score analysis. We demonstrate the performance of the proposed method through Monte Carlo simulations and an empirical application examining the effect of political connections on firm value.

翻译：我们提出了一种贝叶斯倾向得分增强潜在因子模型，用于时间序列截面数据的因果推断。该框架通过纳入潜在因子载荷显式建模处理分配机制，同时结果模型灵活地融合了倾向得分（例如通过分层方式）。与现有方法相比，所提方法具有更强的灵活性，并能捕捉倾向得分层间更多异质性，从而在每一层内实现处理组与对照组间更可信的比较。为进行估计与推断，我们采用近似贝叶斯方法处理贝叶斯倾向得分分析中常见的模型反馈问题。通过蒙特卡洛模拟及一项检验政治关联对企业价值影响的实证应用，我们验证了所提方法的性能。