Estimation and Inference for Synthetic Control Methods with Spillover Effects
Abstract
Estimation and inference procedures for synthetic control methods often do not allow for the existence of spillover effects, which are plausible in many applications. In this paper, we consider estimation and inference for synthetic control methods, allowing for spillover effects. We propose estimators for both direct treatment effects and spillover effects and show that they are asymptotically unbiased. In addition, we propose an inferential procedure and show that it is asymptotically unbiased. Our estimation and inference procedure applies to cases with multiple treated units and/or multiple post-treatment periods, and to ones where the underlying factor model is either stationary or cointegrated. We discuss the bias from misspecified spillover structures and propose a test for correct specification. We apply our method to a classic empirical example that investigates the effect of California's tobacco control program as in Abadie et al. (2010) and find evidence of spillovers. We contrast our method with the pure-donor approach through a sensitivity analysis.
Cite
@article{arxiv.1902.07343,
title = {Estimation and Inference for Synthetic Control Methods with Spillover Effects},
author = {Jianfei Cao and Connor Dowd},
journal= {arXiv preprint arXiv:1902.07343},
year = {2026}
}