Bayesian Propensity Score-Augmented Latent Factor Models for Causal Inference with Time-Series Cross-Sectional Data

TL;DR

This paper introduces a Bayesian latent factor model that combines propensity scores for causal inference in time-series cross-sectional data, offering improved flexibility and heterogeneity capture.

Contribution

It presents a novel Bayesian framework that explicitly models treatment assignment and outcome heterogeneity, enhancing causal inference accuracy in complex panel data.

Findings

Outperforms existing methods in simulations

Provides credible treatment effect estimates

Effectively captures heterogeneity across strata

Abstract

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.