Diverse clusters interconnected by statistical lines and data points, symbolizing randomized experiments.

Decoding Cluster Randomized Experiments: Are We Measuring What We Think We Are?

Reese Marlowe in Business & Economy December 2025 • 4 min read.

"New research sheds light on the hidden complexities of cluster size and its impact on the validity of experiment results, offering a critical perspective for researchers and policymakers."

In the world of economics and social sciences, cluster randomized experiments (CREs) are essential for evaluating the effectiveness of various treatments and programs. Unlike experiments where individual units are randomized, CREs assign interventions at the cluster level—think schools, villages, or healthcare clinics. This approach helps researchers understand how interventions work in real-world settings. However, this article unveils a critical challenge: the often-overlooked impact of varying cluster sizes.

Imagine evaluating a new healthcare program where some clinics are large and others are small. The size of the clinic could influence how effectively the program is implemented and how patients respond. This is where the concept of “non-ignorable cluster sizes” comes into play. If the effect of the treatment differs depending on the size of the cluster, standard analysis methods might lead to skewed or misleading results. A new research paper is diving deep into this issue, offering insights and methods to improve the accuracy of CREs.

This article will explore the complexities of CREs, focusing on how cluster sizes can impact the interpretation of results. We'll break down the key findings of the research paper, discuss the implications for policy and practice, and highlight strategies for conducting more robust and reliable cluster randomized experiments.

The Hidden Problem: Why Cluster Size Matters

Diverse clusters interconnected by statistical lines and data points, symbolizing randomized experiments.

The core issue is that when cluster sizes vary, the average treatment effect can be influenced by the size of the clusters themselves. This is particularly important because it challenges a common assumption in many CREs: that all clusters are created equal. The new research highlights that ignoring these size variations can lead to incorrect conclusions about the true impact of an intervention.

To illustrate, consider a scenario where a new educational program is tested in several schools. If larger schools implement the program more effectively or have different student demographics, the overall results could be skewed. The average treatment effect might appear significant, but this could be driven by the larger schools rather than the program's actual impact. The research emphasizes the need to account for these variations to get a more accurate picture.

Equally-Weighted vs. Size-Weighted Effects: The researchers distinguish between two types of treatment effects.
Equally-weighted: This averages the effect across all clusters, regardless of size.
Size-weighted: This averages the effect based on the number of individuals within each cluster.
Choosing the right method depends on whether you’re interested in the impact on clusters themselves or on the individuals within those clusters.

The research also points out a surprising gap in current practice. A survey of articles published in the American Economic Journal: Applied Economics revealed that many papers don’t explicitly discuss their parameter of interest, and some analyses don’t align with the chosen sampling design. This raises concerns about the validity and interpretation of findings in these studies.

Moving Forward: Best Practices for Cluster Randomized Experiments

This research serves as a wake-up call for researchers and policymakers. By acknowledging the potential impact of cluster sizes, we can design and analyze experiments more effectively, leading to better-informed decisions and more impactful interventions. Understanding these nuances can transform how we approach cluster randomized experiments, ensuring that our insights are both accurate and actionable.

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2204.08356,

Title: Inference For Cluster Randomized Experiments With Non-Ignorable Cluster Sizes

Subject: econ.em stat.me

Authors: Federico Bugni, Ivan Canay, Azeem Shaikh, Max Tabord-Meehan

Published: 18-04-2022

Everything You Need To Know

What are cluster randomized experiments (CREs), and why are they used in economics and social sciences?

Cluster randomized experiments (CREs) are essential in economics and social sciences for evaluating the effectiveness of treatments and programs. Unlike experiments that randomize individual units, CREs assign interventions at the cluster level. Clusters can be schools, villages, or healthcare clinics. This approach enables researchers to understand how interventions function within real-world settings by considering the influence of the group or environment on the outcome.

How can varying cluster sizes in a Cluster Randomized Experiment skew the results of a study?

Varying cluster sizes can significantly influence the average treatment effect in a Cluster Randomized Experiment. When cluster sizes are not considered, it is assumed that all clusters are equal. However, the size of a cluster can impact how effectively a program is implemented or how individuals respond to the treatment. For example, if a new educational program is tested in schools of varying sizes, the overall results might be skewed if larger schools implement the program more effectively. Therefore, failing to account for cluster size variations can lead to inaccurate conclusions about the intervention's actual impact.

What is the difference between equally-weighted and size-weighted effects in the context of Cluster Randomized Experiments?

In Cluster Randomized Experiments, researchers differentiate between equally-weighted and size-weighted effects. An equally-weighted approach averages the treatment effect across all clusters, regardless of their size. On the other hand, a size-weighted approach averages the effect based on the number of individuals within each cluster. The choice between these methods depends on the research question. If the goal is to understand the impact on clusters, equally-weighted analysis is appropriate. If the focus is on the impact on individuals within those clusters, a size-weighted analysis is more suitable.

Why is it important to consider "non-ignorable cluster sizes" in the analysis of Cluster Randomized Experiments?

Considering "non-ignorable cluster sizes" is crucial because the treatment effect can differ based on the size of the cluster. If the effect of the treatment varies with cluster size, standard analysis methods may yield skewed or misleading results. The size of the cluster might influence the effectiveness of program implementation and patient response. By ignoring cluster size variations, researchers risk drawing incorrect conclusions about the true impact of an intervention, which could undermine the validity of the study and the usefulness of its findings for policy and practice.

How can researchers ensure more robust and reliable results when conducting Cluster Randomized Experiments?

Researchers can enhance the robustness and reliability of their Cluster Randomized Experiments by acknowledging and addressing the potential impact of cluster sizes. This involves carefully considering the variation in cluster sizes during the design and analysis phases. Researchers should choose either an equally-weighted or a size-weighted approach, depending on the parameter of interest, as mentioned in the analysis. They should also explicitly discuss the chosen parameter of interest in their publications and ensure that the analysis aligns with the study's sampling design. These practices will lead to more accurate and actionable insights, informing better decisions and more impactful interventions.