Abstract cityscape illustrating synthetic control methodology.

Unlocking Economic Secrets: Can Synthetic Controls Fix Biased Data?

Lena Voss in Business & Economy December 2025 • 4 min read.

"Discover how a groundbreaking method of moments approach promises unbiased economic insights, revolutionizing policy evaluation and investment strategies."

In the realm of economic analysis, constructing a reliable "synthetic control" unit is a common yet challenging endeavor. The traditional approach involves fitting this synthetic control based on outcome variables and covariates during pre-treatment periods. However, research has indicated that this method often falls short of providing asymptotically unbiased results, especially when the fit is imperfect and the number of controls remains fixed. This limitation extends to many related panel methods, particularly when the number of units is constrained.

Enter a new, innovative method that seeks to overcome these challenges: a General Method of Moments (GMM) approach. This novel technique constructs the synthetic control by leveraging units not included in the primary synthetic control group as instrumental variables. The promise? A Synthetic Control Estimator (SCE) of this nature can achieve asymptotic unbiasedness, even when the pre-treatment fit is less than perfect and the number of units is fixed.

Furthermore, the method offers even greater potential. When both pre-treatment and post-treatment time periods extend to infinity, averages of treatment effects can be consistently estimated. This breakthrough could lead to more accurate policy evaluations and a deeper understanding of economic phenomena. To assess the effectiveness of this innovative approach, simulations and empirical applications are conducted, comparing its performance against existing methods in the literature. What follows is an exploration of this method, its potential, and its implications for economic research and policy-making.

What Makes This New Synthetic Control Method Different?

Abstract cityscape illustrating synthetic control methodology.

The Synthetic Control Estimator (SCE), pioneered by Abadie and Gardeazabal (2003) and Abadie et al. (2010), has become a staple in analyzing panel data where a single unit undergoes treatment and remains treated. The core idea is to create a “synthetic” control unit by averaging control units, minimizing differences between the synthetic control and the treated unit during the pre-treatment period. The hope is that this synthetic control mirrors the treated unit's trajectory, had the treatment never occurred.

However, the million-dollar question is: how do you decide which predictors to include and how much weight to give each one? A method of moments perspective offers a principled solution. By choosing predictors that act as moment conditions, theoretically guaranteeing identification, existing weighting techniques provide a solid starting point. Moment conditions are essentially equations that should hold true based on your economic theory or understanding of the system you’re analyzing. Think of it as setting up a series of tests that your synthetic control must pass to be considered valid.

Transparency: Clearly shows how control units are weighted to estimate the treated unit's counterfactual value.
No Specific Trend Requirement: Doesn't require any control unit to have the same trend as the treated unit, unlike difference-in-differences approaches.

Yet, as Ferman et al. (2020) point out, researchers can cherry-pick predictors, leading to statistically significant but potentially misleading results. This new method addresses this issue by providing a theoretically justified approach, reducing ambiguity and limiting specification searching. By grounding the selection process in economic theory, the method aims to produce more robust and reliable results. The use of instrumental variables, other units not included in the control group, to estimate the SCE is the secret sauce of the approach.

The Future of Unbiased Economic Analysis

By addressing the limitations of traditional synthetic control methods, the method of moments approach paves the way for more robust and reliable economic analysis. As researchers grapple with increasingly complex datasets and the need for accurate policy evaluations, this innovative technique offers a valuable tool for unlocking economic secrets and driving better decision-making. This is a powerful tool for any economist or statistician, especially in circumstances where traditional methods fall short.

About this Article -

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2312.01209,

Title: A Method Of Moments Approach To Asymptotically Unbiased Synthetic Controls

Subject: econ.em

Authors: Joseph Fry

Published: 02-12-2023

Everything You Need To Know

What is the core challenge that the traditional Synthetic Control method tries to address in economic analysis?

The core challenge lies in creating a reliable "synthetic control" unit. The traditional approach fits this synthetic control based on outcome variables and covariates during pre-treatment periods. The issue is that this method often falls short of providing asymptotically unbiased results, especially when the fit is imperfect and the number of controls remains fixed. This is a critical limitation in many related panel methods and constrains the accuracy of policy evaluations and investment strategies.

How does the new General Method of Moments (GMM) approach improve upon existing Synthetic Control methods?

The GMM approach constructs the Synthetic Control by leveraging units not included in the primary synthetic control group as instrumental variables. This novel technique ensures the Synthetic Control Estimator (SCE) achieves asymptotic unbiasedness, even with imperfect pre-treatment fits and fixed numbers of units. The method also allows for consistent estimation of averages of treatment effects when pre-treatment and post-treatment periods extend to infinity, leading to more accurate policy evaluations and a deeper understanding of economic phenomena.

What are the key advantages of using a Synthetic Control Estimator (SCE) like the one by Abadie and Gardeazabal (2003) and Abadie et al. (2010), and how does the new method enhance them?

The SCE, pioneered by Abadie and Gardeazabal (2003) and Abadie et al. (2010), provides transparency by clearly showing how control units are weighted. It also does not require control units to have the same trend as the treated unit, unlike difference-in-differences approaches. The new GMM approach enhances these advantages by offering a theoretically justified selection process for predictors. This reduces ambiguity, limits specification searching, and aims to produce more robust and reliable results by grounding the selection process in economic theory.

In the context of the new method, what role do 'moment conditions' and 'instrumental variables' play in building a reliable synthetic control?

Moment conditions, based on economic theory or the understanding of the system being analyzed, serve as tests that the synthetic control must pass to be considered valid. Instrumental variables, which are units not included in the control group, are the 'secret sauce' of the new method. They are used to estimate the Synthetic Control Estimator (SCE), ensuring its asymptotic unbiasedness. By leveraging instrumental variables, the method addresses the limitations of cherry-picking predictors, providing a theoretically justified approach to produce more robust and reliable results.

How can this new method revolutionize economic research and policy-making?

By addressing limitations of traditional Synthetic Control methods, the method of moments approach paves the way for more robust and reliable economic analysis. It allows for more accurate policy evaluations and a deeper understanding of economic phenomena. Simulations and empirical applications compare its performance against existing methods, demonstrating its potential to unlock economic secrets and drive better decision-making. This new approach offers a valuable tool for economists and statisticians, especially where traditional methods fall short, providing more reliable insights for complex datasets.