Synthetic Control Method: Literature Review

The Synthetic Control Method (SCM) has become a cornerstone in the field of causal inference for evaluating the effects of policy interventions, particularly when randomized controlled trials are not feasible. Originating in the early 2000s, SCM has witnessed considerable evolution and application across various domains. This literature review provides a detailed overview of SCM from its inception to its current applications, highlighting seminal works, methodological advancements, and diversification into multiple fields.

Inception and Theoretical Foundation
SCM was formally introduced in their seminal paper assessing the economic impact of terrorism in the Basque Country. They constructed a “synthetic Basque Country” from a weighted combination of other regions to estimate the counterfactual economic performance in the absence of terrorism, laying the groundwork for SCM’s application in causal inference (Abadie & Gardeazabal, 2003).

Abadie, Diamond, and Hainmueller (2010), extended SCM to multi-period case studies, providing a rigorous framework for choosing predictors and weights for the synthetic control units. The method was applied to evaluate the effect of California’s tobacco control program, solidifying SCM’s utility in policy evaluation.

Methodological Advancements
Abadie, Diamond, and Hainmueller (2015), provided a comprehensive synthesis of the methodology, discussing its assumptions, implementation, and statistical properties. This work included a discussion on inferential methods for SCM, addressing the challenge of hypothesis testing with synthetic controls.

Dube and Zipperer (2013), focused on the practical aspects of implementing SCM, including software tools and diagnostics for assessing SCM fits. This contributed to making SCM more accessible to researchers and practitioners.

Extensions and Variations
Xu (2017), introduced the Generalized Synthetic Control Method, expanding SCM’s applicability to settings with multiple treated units and varying treatment timings. This extension broadened the scope of SCM’s application, allowing for more complex intervention analyses.

Ferman, Pinto, and Possebom (2020), addressed the issue of SCM when the synthetic control does not perfectly replicate the pre-treatment path of the treated unit, proposing a robustness check that assesses SCM’s sensitivity to its model specification.

Applications Beyond Economics
Rho, Cummings, and Misra (2023), demonstrated the application of SCM in ensuring data privacy, introducing differentially private synthetic control algorithms. This work illustrates SCM’s potential in addressing modern challenges related to data sensitivity and privacy.

Chen et al. (2023), showcased the integration of SCM with AI and 3D modeling, indicating the method’s versatility and adaptability to technological advancements and creative processes.

Current Challenges and Future Directions
Recent literature points to ongoing challenges in SCM application, including issues of inference and hypothesis testing, sensitivity to model specification, and the search for more generalized and flexible approaches that can handle complex treatment structures and multiple treated units.

Future research directions are likely to focus on improving the method’s robustness, extending its applicability to broader settings, and integrating SCM with cutting-edge technologies and data science techniques. As the demand for rigorous causal inference methods continues to grow across disciplines, SCM’s evolution will remain a key area of interest for researchers and policymakers alike.

This comprehensive overview underscores SCM’s significant contributions to causal inference and policy evaluation, its methodological evolution, and its expanding application across diverse fields, marking it as a pivotal development in empirical research methodologies.