Bayesian Nonparametrics for Principal Stratification with Continuous Post-Treatment Variables

Abstract: Principal stratification provides a causal inference framework for investigating treatment effects in the presence of a post-treatment variable. Principal strata play a key role in characterizing the treatment effect by identifying groups of units with the same or similar values for the potential post-treatment variable under both treatment levels. The literature has focused mainly on binary post-treatment variables. Few papers considered continuous post-treatment variables. In the presence of a continuous post-treatment, a challenge is how to identify and characterize meaningful coarsening of the latent principal strata that lead to interpretable principal causal effects. This paper introduces the confounders-aware shared-atom Bayesian mixture, a novel approach for principal stratification with binary treatment and continuous post-treatment variables. Our method leverages Bayesian nonparametric priors with an innovative hierarchical structure for the potential post-treatment variable that overcomes some of the limitations of previous works. Specifically, the novel features of our method allow for (i) identifying coarsened principal strata through a data-adaptive approach and (ii) providing a comprehensive quantification of the uncertainty surrounding stratum membership. Through Monte Carlo simulations, we show that the proposed methodology performs better than existing methods in characterizing the principal strata and estimating principal effects of the treatment. Finally, our proposed model is applied to a case study in which we estimate the causal effects of US national air quality regulations on pollution levels and health outcomes.