Factor-augmented Bayesian treatment effects models for panel outcomes

TL;DR

This paper introduces a novel factor-augmented Bayesian model for estimating dynamic treatment effects in panel data, effectively separating endogeneity from longitudinal associations to improve inference accuracy.

Contribution

It presents a new flexible modeling approach that accounts for endogeneity and longitudinal effects, enabling unbiased estimation of treatment impacts over time.

Findings

The method performs well on simulated data.

Reanalysis of maternity leave data illustrates its practical utility.

Provides unbiased estimates of treatment effects in panel outcomes.

Abstract

We propose a new, flexible model for inference of the effect of a binary treatment on a continuous outcome observed over subsequent time periods. The model allows to seperate association due to endogeneity of treatment selection from additional longitudinal association of the outcomes and hence unbiased estimation of dynamic treatment effects. We investigate the performance of the proposed method on simulated data and employ it to reanalyse data on the longitudinal effects of a long maternity leave on mothers' earnings after their return to the labour market.