Illustration of a researcher navigating a maze of data.

Unraveling Difference-in-Differences: A Guide to Better Research Outcomes

Rhea Morgan in Health & Wellness December 2025 • 4 min read.

"Is matching really improving your research? Learn how to avoid common pitfalls in difference-in-differences studies and ensure reliable results."

In health care research, difference-in-differences is widely used to evaluate initiatives like Medicaid expansions, payment reforms, and Accountable Care Organizations. This approach compares the outcomes of a treatment group and a control group before and after an intervention. The key assumption is that, without the intervention, both groups would have followed parallel trends. But what happens when this assumption fails?

To address non-parallel trends, researchers often use matching on pre-treatment outcomes. The idea is to create comparable groups before applying the difference-in-differences method. However, simulations suggest that this approach doesn't always eliminate or reduce bias, leaving researchers wondering when and why it works.

This article dives into the complexities of difference-in-differences and matching. Using Medicaid claims data from Oregon, we'll explore how unobservable factors—fixed effects and random error—affect the bias of matching on pre-treatment outcomes. You'll learn how to identify potential pitfalls and improve the reliability of your research.

Sources of Bias: Unveiling the Unobservables

Illustration of a researcher navigating a maze of data.

The effectiveness of matching on pre-treatment outcomes depends on how well it addresses imbalances between the treatment and control groups. However, similar outcome levels or trends don't guarantee balanced trend effects. Two key unobservables can significantly impact your results:

Imagine you're trying to match clinics based on their pre-intervention patient visit rates. Some clinics might have consistently higher visit rates due to better patient engagement strategies (a fixed effect), while others might experience short-term spikes due to a particularly bad flu season (random error). Matching alone can't distinguish between these underlying factors.

Fixed Effects (Level Effects): These are time-invariant differences among observations, such as inherent differences in patient populations or management practices.
Random Error: This refers to short-term, random fluctuations in outcomes that can obscure underlying trends.

The distribution of these unobservables can significantly affect the performance of matching combined with difference-in-differences. For example, a high standard deviation in the random error term can make it difficult to differentiate between short-term fluctuations and genuine outcome trends. Similarly, the distribution of fixed effects can create bias, especially when matching on pre-treatment outcome levels.

Practical Steps to Improve Your Research

So, how can you navigate these challenges and ensure the reliability of your difference-in-differences studies? Here are some practical steps:

Researchers should report estimates from both unadjusted and propensity-score matching adjusted difference-in-differences. Compare results for matching on outcome levels and trends and examine outcome changes around intervention begin to assess remaining bias.

By acknowledging the limitations of matching and employing these strategies, you can strengthen your research and gain a more accurate understanding of the true effects of interventions.

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: 10.1007/s10742-018-0189-0, Alternate LINK

Title: Difference-In-Differences And Matching On Outcomes: A Tale Of Two Unobservables

Subject: Public Health, Environmental and Occupational Health

Journal: Health Services and Outcomes Research Methodology

Publisher: Springer Science and Business Media LLC

Authors: Stephan Lindner, K. John Mcconnell

Published: 2018-10-03

Everything You Need To Know

What is difference-in-differences and why is it important?

The core idea behind difference-in-differences is to compare the changes in outcomes between a treatment group and a control group, both before and after an intervention. This approach is vital because it helps researchers isolate the effect of the intervention by accounting for pre-existing trends. In healthcare, it is used to evaluate initiatives like Medicaid expansions, payment reforms, and Accountable Care Organizations. This method assumes that in the absence of the intervention, both groups would have followed parallel trends, allowing researchers to attribute any observed differences to the intervention itself.

What is the purpose of matching on pre-treatment outcomes?

Matching on pre-treatment outcomes aims to create comparable treatment and control groups before applying difference-in-differences. This is done to balance the groups based on their characteristics prior to the intervention. However, it is important to note that matching doesn't always eliminate bias because of unobservable factors. Using Medicaid claims data from Oregon can help demonstrate how these unobservable factors can affect the bias.

What are Fixed Effects and Random Error and why are they relevant?

Fixed Effects represent time-invariant differences among observations, like inherent differences in patient populations or management practices, these impact the reliability of the research when matching. Random Error refers to short-term, random fluctuations in outcomes that can obscure underlying trends. Examples include clinics that have consistently higher visit rates due to better patient engagement strategies (Fixed Effect) or short-term spikes due to a particularly bad flu season (Random Error). Matching can't distinguish between these underlying factors.

How do Fixed Effects and Random Error influence research outcomes?

The distribution of unobservables affects the performance of matching combined with difference-in-differences. A high standard deviation in the Random Error term can make it difficult to differentiate between short-term fluctuations and genuine outcome trends. Similarly, the distribution of Fixed Effects can create bias, especially when matching on pre-treatment outcome levels. These factors can lead to inaccurate conclusions about the intervention's effect.

How can researchers improve the reliability of their research when using difference-in-differences?

To improve the reliability of difference-in-differences studies, researchers should focus on understanding the potential impact of Fixed Effects and Random Error. Careful consideration of how matching interacts with these unobservable factors is necessary. You can achieve this by conducting simulations and sensitivity analyses to assess the robustness of the results. In addition, ensuring the parallel trends assumption holds is also crucial.