Interconnected pathways merging into a glowing sphere, representing policy outcomes.

Beyond Borders: How Synthetic Control Methods are Revolutionizing Policy Evaluation

Jordan Keane in Business & Economy December 2025 • 4 min read.

"Unlock the secrets of policy evaluation with Synthetic Controls—a method reshaping economic analysis and offering new insights into global events."

In an era defined by rapid change and complex challenges, policymakers and economists alike are constantly seeking better tools to evaluate the impact of interventions. Traditional methods often fall short when trying to isolate the effects of a specific policy amidst a sea of confounding factors. Enter Synthetic Control Methods (SCM), a groundbreaking approach that’s redefining the landscape of policy evaluation.

At its core, SCM allows researchers to construct a ‘synthetic’ version of the entity affected by a policy—be it a country, state, or city—by combining various control units to mimic the characteristics of the treated entity before the intervention. This synthetic unit then serves as a counterfactual, illustrating what would have likely happened in the absence of the policy. The divergence between the actual outcome and the synthetic outcome reveals the policy’s true impact.

Now, researchers are pushing the boundaries of SCM further than ever. By incorporating multiple outcomes into the synthetic control framework, they're enhancing the reliability and applicability of this powerful tool. This evolution not only refines our understanding of policy effects but also opens doors for evaluating interventions in scenarios previously deemed too complex or data-scarce.

Why Multiple Outcomes Matter: Enhancing Precision in Policy Analysis

Interconnected pathways merging into a glowing sphere, representing policy outcomes.

Traditional SCM primarily focuses on a single outcome variable, such as GDP or employment rates. However, policies often have multifaceted effects that ripple across various sectors. By expanding the SCM framework to include multiple related outcomes, researchers can capture a more holistic view of a policy's impact.

The key advantage of this multiple-outcome approach lies in its ability to improve the precision and robustness of treatment effect estimations. Here’s how:

Enhanced Matching: Incorporating multiple outcomes allows for a more comprehensive matching process during the construction of the synthetic control. This reduces the risk of overfitting, where the synthetic unit closely matches the treated unit on a single outcome but fails to capture broader economic trends.
Increased Reliability: By considering related outcomes, researchers can validate the consistency of the treatment effect across different indicators. This strengthens the credibility of the findings and provides a more nuanced understanding of the policy's consequences.
Addressing Data Scarcity: In situations where pre-treatment data is limited, leveraging multiple outcomes can compensate for the lack of historical information. This makes SCM applicable to a wider range of scenarios, including those with short observation windows.

Consider, for instance, the reunification of Germany in 1990—a classic example often analyzed using SCM. Instead of solely focusing on GDP per capita, a multiple-outcome approach could incorporate indicators such as social expenditure, energy supply, and trade openness. This provides a richer, more detailed understanding of the reunification's economic and social consequences.

The Future of Policy Evaluation: Broader Applications and Refined Insights

As Synthetic Control Methods continue to evolve, particularly with the incorporation of multiple outcomes, their potential to inform policy decisions and enhance governance worldwide is immense. Whether it's assessing the impact of economic reforms, evaluating public health interventions, or understanding the consequences of geopolitical events, SCM offers a powerful framework for evidence-based analysis. By providing robust, reliable, and nuanced insights, SCM empowers policymakers to make more informed decisions and navigate the complexities of our rapidly changing world with greater confidence.

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Everything You Need To Know

What is the core concept behind Synthetic Control Methods (SCM) and how does it work?

At its core, Synthetic Control Methods (SCM) construct a 'synthetic' version of the entity (country, state, or city) affected by a policy. This is achieved by combining various control units to mirror the characteristics of the treated entity before the intervention. This synthetic unit acts as a counterfactual, showing what would have happened without the policy. The difference between the actual outcome and the synthetic outcome reveals the policy's true impact, offering a robust way to evaluate policy effectiveness by isolating its effects from other factors.

Why is incorporating multiple outcomes into the Synthetic Control framework considered an advancement?

Expanding the Synthetic Control framework to include multiple outcomes significantly enhances the reliability and applicability of this powerful tool. Traditional SCM often focuses on a single outcome, like GDP. However, policies have multifaceted effects. By considering related outcomes such as social expenditure, energy supply, and trade openness, researchers can achieve a more comprehensive matching process during the construction of the synthetic control. This reduces the risk of overfitting and provides a more nuanced understanding of the policy's consequences, addressing data scarcity and improving the overall precision of treatment effect estimations.

How does the use of multiple outcomes in SCM improve the reliability of policy evaluation?

Incorporating multiple outcomes strengthens the reliability of policy evaluations in a few key ways. Enhanced matching during the creation of the synthetic control helps to reduce the risk of overfitting, which ensures that the synthetic unit accurately reflects pre-intervention trends. Considering related outcomes allows researchers to validate the consistency of the treatment effect across different indicators. This provides a more nuanced understanding of the policy's consequences, making the findings more credible. For instance, when evaluating the reunification of Germany, multiple outcomes such as social expenditure and trade openness provide a richer perspective than focusing solely on GDP.

What are some real-world applications where Synthetic Control Methods (SCM) can be used to evaluate policy impacts?

Synthetic Control Methods (SCM) are incredibly versatile and can be applied to a wide range of policy evaluations. They are suitable for assessing the impact of economic reforms, providing insights into public health interventions, and understanding the consequences of geopolitical events. For example, SCM can be used to evaluate the effects of economic policies on various sectors. It can also assess the impact of public health interventions by analyzing multiple health-related outcomes. Furthermore, SCM can offer valuable insights into the economic and social consequences of significant events, such as the reunification of Germany, allowing for a deeper understanding of their broader implications.

What are the primary benefits of using Synthetic Control Methods (SCM) over traditional methods in policy evaluation?

Synthetic Control Methods (SCM) offer several key advantages over traditional policy evaluation methods. SCM excels at isolating the effects of a specific policy amidst complex, confounding factors. This is achieved by constructing a synthetic control group, which mimics the characteristics of the treated entity before the policy intervention. This allows researchers to compare the actual outcomes to a counterfactual scenario. The multiple-outcome approach further enhances SCM by improving the precision of treatment effect estimations. This provides a more holistic view of a policy's impact. SCM provides robust, reliable, and nuanced insights, empowering policymakers to make more informed decisions, especially in scenarios with data scarcity.