Illustration of data streams balanced on a scale, symbolizing economic analysis.

Unlocking Economic Truths: Can We Really Trust Our Data?

Samir D’Costa in Business & Economy September 2025 • 4 min read.

"New research questions the assumptions behind common economic analyses, urging a closer look at how we interpret results."

In the world of economics, accurately determining cause and effect is crucial for effective policymaking and informed decision-making. For years, economists have relied on two primary assumptions to identify the Average Treatment Effect on the Treated (ATET): unconfoundedness and common trends. However, a recent study casts doubt on whether these assumptions always hold water, proposing a new method for testing their validity.

The unconfoundedness assumption suggests that treatment assignment and post-treatment outcomes are independent when considering control variables and pre-treatment outcomes. This approach encourages economists to include pre-treatment data in their analyses to create a more accurate picture. Conversely, the common trends assumption posits that trends and treatment assignments are independent, conditional on control variables. This assumption leads to the use of the Difference-in-Differences (DiD) approach, comparing outcomes between treatment and control groups before and after the intervention.

Given that these assumptions are non-nested and their plausibility can be ambiguous, Martin Huber and Eva-Maria Oeß have introduced an overidentification test that jointly assesses unconfoundedness and common trends. Their method employs a doubly robust statistic, enhanced by machine learning to control for confounding variables in a data-driven way. This innovative approach promises to bring more rigor and reliability to economic analysis.

Why Traditional Methods Might Be Leading Us Astray

Illustration of data streams balanced on a scale, symbolizing economic analysis.

Traditional methods rely heavily on either the unconfoundedness assumption or the common trends assumption. The problem? Neither assumption is foolproof, and both can be easily undermined by hidden variables or flawed data. For example, the unconfoundedness assumption falls apart if there are unobserved factors influencing both the treatment assignment and the outcome. Similarly, the common trends assumption fails if the treatment group and the control group have different underlying trends, irrespective of the treatment itself.

Huber and Oeß highlight that these assumptions are not merely theoretical concerns but have real-world implications. The choice between them can significantly impact the conclusions drawn from economic studies. To address these concerns, their new test offers a way to evaluate whether these assumptions hold in a given context, providing a more reliable foundation for analysis.

Unconfoundedness: Assumes that after controlling for observed covariates, the treatment assignment is independent of potential outcomes.
Common Trends: Assumes that, in the absence of treatment, the treatment and control groups would have followed parallel trends.
Non-Nested Assumptions: Neither assumption implies the other, making it crucial to test them jointly.

The researchers also explore causal models that either support or undermine these assumptions. These models provide a visual framework for understanding how different factors can interact to influence the validity of economic analyses. By examining these models, economists can gain deeper insights into the potential pitfalls of relying solely on unconfoundedness or common trends.

The Path Forward: Robust Testing for Reliable Insights

The joint test developed by Huber and Oeß represents a significant step forward in ensuring the reliability of economic analyses. By employing a doubly robust statistic combined with machine learning, this method offers a powerful tool for economists and policymakers alike. As the study demonstrates through simulations and empirical examples, the ability to test these fundamental assumptions can lead to more accurate and trustworthy conclusions, ultimately improving the quality of economic decision-making.

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2404.16961,

Title: A Joint Test Of Unconfoundedness And Common Trends

Subject: econ.em

Authors: Martin Huber, Eva-Maria Oeß

Published: 25-04-2024

Everything You Need To Know

What are the two primary assumptions that economists have traditionally relied on to determine the Average Treatment Effect on the Treated (ATET), and why are they problematic?

Economists have traditionally relied on two main assumptions: unconfoundedness and common trends. The unconfoundedness assumption suggests that treatment assignment and post-treatment outcomes are independent when considering control variables and pre-treatment outcomes. This means any factor that might influence both the treatment and outcome must be controlled for, ideally using pre-treatment data. The common trends assumption posits that the trends in treatment assignment and outcomes are independent, conditional on control variables. The problem is that neither assumption is guaranteed to hold true. The unconfoundedness assumption fails when there are unobserved variables that influence both the treatment and the outcome, and the common trends assumption can be violated if the treatment and control groups have different underlying trends regardless of the treatment itself. These violations can lead to inaccurate conclusions about the impact of interventions.

Can you explain the concept of unconfoundedness and its implications in economic studies?

The unconfoundedness assumption is a critical concept in economic analysis. It assumes that after accounting for observable covariates (control variables), the assignment to the treatment is independent of potential outcomes. In simpler terms, it means that the treatment assignment is random, and there are no hidden variables that are influencing both the treatment and the outcome of interest. To satisfy this assumption, economists often include pre-treatment data in their analysis. However, the validity of the unconfoundedness assumption hinges on the completeness of the control variables and the absence of hidden variables. If key variables are omitted, or if there are unobserved factors, the assumption is violated, and the estimated treatment effect will be biased.

How does the Difference-in-Differences (DiD) approach relate to the common trends assumption, and what are the potential pitfalls of relying on this approach?

The Difference-in-Differences (DiD) approach is directly linked to the common trends assumption. The common trends assumption posits that the trends in treatment and control groups would have been parallel if the treatment had not occurred. DiD exploits this by comparing the changes in outcomes over time between the treatment and control groups. The assumption allows researchers to isolate the impact of the treatment by removing the common trends. The main pitfall of relying on DiD is that the common trends assumption can be easily violated. This happens if the treatment and control groups had different underlying trends even before the treatment. If the groups were diverging, the DiD estimate of the treatment effect would be inaccurate.

What is the significance of the overidentification test developed by Huber and Oeß, and how does it improve the reliability of economic analysis?

The overidentification test developed by Martin Huber and Eva-Maria Oeß is significant because it jointly assesses the validity of the unconfoundedness and common trends assumptions, which is a novel approach. This method offers a way to test these fundamental assumptions in a given context, thus providing a more reliable foundation for analysis. They introduced a doubly robust statistic, enhanced by machine learning to control for confounding variables in a data-driven way. By jointly testing these assumptions, Huber and Oeß's method reduces the risk of drawing incorrect conclusions based on flawed assumptions. It offers economists and policymakers a robust tool for ensuring the accuracy and trustworthiness of economic studies, leading to better-informed decisions.

In practical terms, how can the choice between unconfoundedness and common trends impact the conclusions drawn from economic studies, and what alternatives are being proposed?

The choice between unconfoundedness and common trends can drastically impact the results of economic studies. If the unconfoundedness assumption is violated, economists may wrongly attribute the treatment effect to the observed variables. If the common trends assumption is violated, the impact can be overestimated or underestimated. The alternative proposed by Huber and Oeß is a joint test of both assumptions. This overidentification test helps evaluate whether these assumptions hold in a given context, leading to more accurate and trustworthy conclusions. The test uses a doubly robust statistic, enhanced by machine learning, providing a more reliable foundation for analysis and improving the quality of economic decision-making. This test allows researchers to identify whether the assumptions hold true, improving the reliability of conclusions.