A1532
Title: Bayesian causal inference in the presence of endogenous selection into treatment and spillovers
Authors: Duong Trinh - University of Graz (Austria) [presenting]
Abstract: A new approach is introduced that harnesses network or spatial data to identify and estimate direct and indirect causal effects in the presence of selection-on-unobservables and spillovers. The proposed framework nests the Generalised Roy model to explicitly account for endogenous selection into treatment and goes beyond to capture spillovers through exposure mapping to neighbours' treatment. This allows for heterogeneous effects across individuals and enables exploration of various policy-relevant treatment effects. We develop Bayesian estimators based on data augmentation methods, offering efficient computation and proper uncertainty quantification, which is supported by simulation experiments. We apply the method to evaluate the Opportunity Zones (OZ) program, which aims to stimulate economic growth in distressed U.S. census tracts through tax incentives. The results show both direct and indirect positive impacts on housing unit growth in designated Qualified Opportunity Zones (QOZs), but unselected tracts (non-QOZs) experience no beneficial spillovers, remaining at a disadvantage. Moreover, the model predicts that offering investment tax credits to non-QOZs would lead to negative outcomes, making the program's expansion to these areas ineffective.
