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Synthetic Controls with spillover effects: A comparative study

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arxiv 2405.01645 v1 pith:MTOFLNY6 submitted 2024-05-02 econ.EM

classification econ.EM
keywords controlmethodperformancesyntheticinclusiveintroducediterativemodifications
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Iterative Synthetic Control Method is introduced in this study, a modification of the Synthetic Control Method (SCM) designed to improve its predictive performance by utilizing control units affected by the treatment in question. This method is then compared to other SCM modifications: SCM without any modifications, SCM after removing all spillover-affected units, Inclusive SCM, and the SP SCM model. For the comparison, Monte Carlo simulations are utilized, generating artificial datasets with known counterfactuals and comparing the predictive performance of the methods. Generally, the Inclusive SCM performed best in all settings and is relatively simple to implement. The Iterative SCM, introduced in this paper, was in close seconds, with a small difference in performance and a simpler implementation.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning about Treatment Effects in Panels under Unknown Interference

    econ.EM 2026-08 conditional novelty 6.0 of 10

    Under unknown interference, the sharp identified set for a panel treatment effect is characterized exactly by feasibility of a finite linear system, enabling uniform candidatewise bootstrap inference.

  2. Accounting for spillover when using the augmented synthetic control method: estimating the effect of localized COVID-19 lockdowns in Chile

    stat.ME 2025-04 conditional novelty 4.0 of 10

    ASCM-SC stratifies donor units by neighbor-treatment status to estimate direct, total, and spillover effects under interference; in the Chilean COVID-19 application, most confidence intervals include zero.

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