Identification and Bayesian Inference for Synthetic Control Methods with Spillover Effects
Abstract
The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when the number of treated units is small. It relies on the stable unit treatment value assumption (SUTVA), ruling out spillover effects. However, interventions often affect not only treated but also untreated units. This study proposes a novel panel data method that extends standard SCM to account for spillovers and estimate both treatment and spillover effects. The approach extends the SCM framework by incorporating a spatial autoregressive (SAR) panel data model that captures spillover patterns across units. We also develop a Bayesian inference procedure using horseshoe priors for regularization. We apply the proposed method to two empirical studies: (i) evaluating the effect of the California tobacco tax on cigarette consumption, and (ii) assessing the economic impact of the 2011 Sudan division on GDP per capita.
Cite
@article{arxiv.2408.00291,
title = {Identification and Bayesian Inference for Synthetic Control Methods with Spillover Effects},
author = {Shosei Sakaguchi and Hayato Tagawa},
journal= {arXiv preprint arXiv:2408.00291},
year = {2026}
}