Global policy evaluation through synthetic control analysis.

Beyond Borders: How 'Synthetic Control' Analysis is Revolutionizing Global Policy Evaluation

Reese Marlowe in Politics & Policy September 2025 • 5 min read.

"Discover how a groundbreaking statistical technique is enhancing the reliability of policy impact assessments worldwide, offering new insights for informed decision-making."

Imagine trying to determine whether a new education program truly boosts student performance or if a public health initiative effectively reduces disease rates. Traditionally, economists and policymakers have relied on various statistical methods to assess the impact of their interventions. However, many of these approaches fall short when dealing with complex, real-world scenarios. This is where the 'synthetic control' method steps in, offering a more robust and nuanced way to evaluate policy outcomes.

The synthetic control method, initially developed by Alberto Abadie and Guido Gardeazabal in 2003, provides a framework for estimating the impact of a specific intervention—be it a policy change, economic reform, or other event—on a single entity, such as a country, state, or city. It works by creating a 'synthetic' version of the entity that did not experience the intervention, using a weighted combination of other similar entities that were not exposed to the policy change.

A new research paper generalizes the synthetic control method to handle multiple outcomes simultaneously, enhancing its applicability and precision. This advancement is particularly valuable when assessing policies with multifaceted impacts or when pre-intervention data is limited. By considering a broader range of related outcomes, this refined approach offers a more reliable and comprehensive understanding of policy effectiveness, opening new avenues for evidence-based decision-making across various sectors.

What is Synthetic Control and Why Does It Matter for Policy?

Global policy evaluation through synthetic control analysis.

At its core, the synthetic control method addresses the challenge of isolating the true effect of an intervention from other factors that might influence outcomes. Traditional methods often struggle to account for the unique characteristics of the entity being studied and the complex interplay of various factors. For example, if a city implements a new anti-poverty program, it can be difficult to determine whether any observed improvements in poverty rates are due to the program itself or to broader economic trends.

The synthetic control method tackles this problem by constructing a counterfactual scenario: what would have happened to the entity if it had not experienced the intervention? This counterfactual is created by selecting a combination of control units (other cities, states, or countries) and assigning weights to each unit to create a 'synthetic' version of the treated entity. The weights are chosen to match the pre-intervention characteristics of the treated entity as closely as possible, based on a set of relevant outcome variables and predictors.

Objective Creation of Counterfactual: It methodically generates a synthetic twin of the intervention target by combining multiple control units.
Multi-dimensional Balancing: Ensures the synthetic control closely matches the real entity across multiple pre-intervention characteristics, not just a single metric.
Transparency: Provides clear, defensible weights, enhancing interpretability and trust in the results.
Adaptability: Suitable for assessing policy impacts in various fields like economics, public health, and environmental science.
Mitigation of Bias: Reduces the risk of selection bias and omitted variable bias, providing more reliable estimates of policy effects.

Once the synthetic control is constructed, the impact of the intervention can be estimated by comparing the outcomes of the treated entity with those of its synthetic counterpart after the intervention. The difference between the two represents the estimated effect of the policy. This approach has been successfully applied to evaluate a wide range of interventions, from tobacco control programs to economic reforms, offering valuable insights for policymakers and researchers alike.

The Future of Policy Evaluation: Embracing Comprehensive Analysis

The generalized synthetic control method represents a significant step forward in the field of policy evaluation, offering a more robust and versatile tool for assessing the impact of interventions in complex real-world settings. By considering multiple outcomes simultaneously and addressing limitations in pre-intervention data, this refined approach promises to enhance the reliability and comprehensiveness of policy assessments across diverse sectors. As policymakers and researchers increasingly embrace evidence-based decision-making, the synthetic control method is poised to play a pivotal role in shaping effective and impactful policies worldwide.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2304.02272,

Title: Synthetic Controls With Multiple Outcomes

Subject: econ.gn q-fin.ec

Authors: Wei Tian, Seojeong Lee, Valentyn Panchenko

Published: 05-04-2023

Everything You Need To Know

What is the synthetic control method and how does it improve policy evaluation?

The synthetic control method is a statistical technique used to evaluate the impact of a specific intervention, such as a policy change, on a single entity. It creates a 'synthetic' version of the entity that did not experience the intervention. This is done by using a weighted combination of similar entities that were not exposed to the policy change. This approach is an improvement over traditional methods because it addresses the challenge of isolating the true effect of an intervention from other factors that might influence outcomes, offering a more robust and nuanced way to evaluate policy outcomes, improving the reliability of impact assessments.

How does the synthetic control method work to create a 'synthetic' version of a city or country?

The synthetic control method constructs a counterfactual scenario to estimate the impact of a policy. It selects a combination of control units (other cities, states, or countries) and assigns weights to each unit to create a 'synthetic' version of the treated entity. These weights are chosen to match the pre-intervention characteristics of the treated entity as closely as possible, based on a set of relevant outcome variables and predictors. The pre-intervention data and the characteristics of the control units are the basis for creating this synthetic counterpart.

In what ways does the generalized synthetic control method improve upon the original approach?

The generalized synthetic control method handles multiple outcomes simultaneously, enhancing its applicability and precision. This refinement is particularly valuable when assessing policies with multifaceted impacts or when pre-intervention data is limited. The method considers a broader range of related outcomes, offering a more reliable and comprehensive understanding of policy effectiveness and opening new avenues for evidence-based decision-making across various sectors. It addresses the limitations by providing the ability to include more variables in the analysis.

What are the advantages of using the synthetic control method compared to traditional policy evaluation methods?

The synthetic control method offers several advantages. It creates an objective counterfactual, providing clear, defensible weights, enhancing interpretability and trust in the results. It ensures the synthetic control closely matches the real entity across multiple pre-intervention characteristics, not just a single metric. It's adaptable for various fields like economics and public health and mitigates selection and omitted variable bias. It provides more reliable estimates of policy effects by focusing on the specific characteristics of the entity being studied and accounting for the complex interplay of various factors which is often overlooked by traditional methods.

What are the practical applications of the synthetic control method in the real world, and what kind of policies can it help evaluate?

The synthetic control method has been successfully applied to a wide range of interventions. It's useful for evaluating tobacco control programs and economic reforms, offering valuable insights for policymakers. This method allows the evaluation of the impact of policies across various sectors, from economics and public health to environmental science. It enables researchers and policymakers to assess the effects of interventions more precisely and reliably, contributing to evidence-based decision-making.