Inside the Heat Dome: analyzing heatstroke hospitalizations using Bayesian synthetic control with spatially augmented priors

Music City Center 

The synthetic control (SC) method is widely used to estimate causal effects using panel data. However, the classical SC framework does not account for spatial dependence and spill-over effects common when observational units represent spatial entities such as cities, counties, or regions. Spatial correlation and latent spatial confounding can bias estimates, yet little research has addressed these issues systematically, and simulation studies in this context remain scarce.

We propose the spatially-augmented Bayesian synthetic control (SA-BSC), which integrates geographic distance into spike-and-slab priors on donor weights. Two specifications are available: distance-to-binary (D2B), where a control unit's inclusion probability decays with distance, and distance-to-variance (D2V), which exponentially shrinks the prior variance of distant donors. Using this approach, we can encompass additional information into the synthetic control estimation, leveraging the flexibility of semiparametric spatial priors for weights estimation. Through extensive simulations varying the pre-treatment window length, spatial autocorrelation, and magnitude of spill-over effects, we find that SA-BSC substantially reduces root-mean-squared error and improves posterior-interval coverage compared to standard non-spatial synthetic control methods.

We illustrate the application of SA-BSC with a large-scale observational study examining acute heat-stroke hospitalizations among an open cohort of fee-for-service Medicare beneficiaries in the contiguous United States, covering 34.5 million individuals from 2000 to 2016. Daily maximum heat-index data are linked to residential ZIP codes, defining heat waves as periods of two or more consecutive days exceeding the local 95th percentile. Each exposed ZIP-day constitutes a treated unit, with counterfactual donors constructed from contemporaneously unexposed ZIP codes. SA-BSC provides spatially coherent counterfactual outcomes and robust, interpretable causal estimates, highlighting its value for observational studies with complex spatial structures.

Gun violence

Heatwaves

Causal inference

Spatial statistics

Synthetic controls

Environmental health 

Section on Statistics and the Environment