Quasi-Experiments: Navigating Uncertainty and Bias in Social Science Research

Kai Mendoza in Mind & Education August 2025 • 4 min read.

"A Design-Based Approach to Enhance Reliability in Quasi-Experimental Studies"

In the realm of social sciences, understanding cause-and-effect relationships often relies on experimental data. However, true experiments with randomly assigned treatments are not always feasible. This is where quasi-experiments come into play. Quasi-experimental designs attempt to mimic experimental conditions but often grapple with inherent uncertainties and biases.

Traditional statistical methods often treat data as a sample drawn from a larger population, which can feel unnatural when dealing with complete datasets, such as all U.S. states or all individuals in a country. An alternative approach, known as design-based inference, focuses on the stochastic realization of treatment across units. This method acknowledges that uncertainty arises from the specific way treatments happen to be distributed, rather than from sampling variability.

A significant challenge in quasi-experiments is the potential for selection bias, where units self-select into treatment groups based on factors that also influence the outcome. This creates a tangled web of causation that can be difficult to unravel. New research offers a design-based framework specifically tailored to address these concerns, providing tools to assess and mitigate the impact of selection bias on causal inferences.

Unpacking Design-Based Uncertainty: A New Lens for Quasi-Experiments

Threads of social relationships being untangled

The core idea behind this framework is to treat the assignment of treatments as a partially random process. Instead of assuming treatments are purely random, it acknowledges that idiosyncratic factors play a role, while still allowing for the possibility of selection bias. Units may have different probabilities of receiving treatment, reflecting the reality that some are more inclined or susceptible to treatment than others.

This approach allows researchers to explore how sensitive their conclusions are to different assumptions about selection bias. By characterizing potential biases and distortions to inference, the framework provides a rigorous foundation for conducting sensitivity analyses. These analyses help to determine how much selection bias would need to be present to overturn the primary findings.

Stochastic Treatment Assignment: Recognizes that treatments are not always purely random but influenced by idiosyncratic factors.
Variable Treatment Probabilities: Accounts for the fact that units may have different probabilities of receiving treatment.
Sensitivity Analysis: Provides tools to assess how robust conclusions are to potential selection bias.

Consider the example of state-level policy changes, such as Medicaid expansions. While the decision to expand Medicaid might seem like a clear-cut policy choice, it is often influenced by a complex interplay of political, economic, and social factors. Some states may be more predisposed to expansion due to their political leanings or the specific needs of their populations. By acknowledging these varying probabilities, researchers can more accurately assess the causal impact of Medicaid expansion on health outcomes.

Moving Forward: Embracing Rigor and Transparency in Quasi-Experimental Research

The design-based framework offers a powerful toolkit for researchers seeking to draw causal inferences from quasi-experimental data. By explicitly addressing the potential for selection bias and providing methods for sensitivity analysis, it promotes more rigorous and transparent research practices. This approach not only enhances the credibility of findings but also fosters a deeper understanding of the complex relationships that shape our social world.

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2008.00602,

Title: Design-Based Uncertainty For Quasi-Experiments

Subject: econ.em stat.me

Authors: Ashesh Rambachan, Jonathan Roth

Published: 02-08-2020

Everything You Need To Know

What are quasi-experiments, and why are they used in social science research if true experiments are preferable?

Quasi-experiments are research designs that attempt to mimic experimental conditions when random assignment of treatments isn't feasible. They are crucial in social sciences because real-world scenarios often prevent true experiments. However, quasi-experiments introduce uncertainties and biases that must be carefully addressed. The key difference lies in how treatment is assigned; in true experiments it's random, while in quasi-experiments, it's often influenced by other factors, leading to potential selection bias. Design-based inference is used to address this.

What is design-based inference, and how does it differ from traditional statistical methods in the context of quasi-experiments?

Design-based inference is an approach that focuses on the stochastic realization of treatment across units. Unlike traditional statistical methods that treat data as a sample from a larger population, design-based inference acknowledges that uncertainty arises from how treatments are distributed. This is particularly relevant when dealing with complete datasets, such as all U.S. states, where traditional sampling assumptions don't apply. It shifts the focus from sampling variability to the specific way treatments happen to be assigned, accounting for factors influencing that assignment.

What is selection bias, and why is it a significant challenge in quasi-experiments?

Selection bias occurs when units self-select into treatment groups based on factors that also influence the outcome, creating a tangled web of causation. This is a significant challenge in quasi-experiments because it makes it difficult to determine whether the observed effect is due to the treatment itself or to the pre-existing differences between the treatment and control groups. Addressing selection bias is crucial for drawing valid causal inferences from quasi-experimental data and design based analysis are necessary.

How does the design-based framework address the potential for selection bias in quasi-experiments, and what are its key components?

The design-based framework addresses selection bias by treating the assignment of treatments as a partially random process, acknowledging that idiosyncratic factors play a role. It allows for variable treatment probabilities, reflecting the reality that some units are more inclined or susceptible to treatment than others. The framework provides tools for sensitivity analysis to assess how robust conclusions are to potential selection bias. Key components include stochastic treatment assignment, variable treatment probabilities, and sensitivity analysis.

Can you provide an example of how this framework can be applied in a real-world scenario, such as state-level policy changes?

Consider the example of state-level policy changes like Medicaid expansions. The decision for a state to expand Medicaid is influenced by various political, economic, and social factors. Some states may be more predisposed to expansion due to their political leanings or the needs of their populations. By acknowledging these varying probabilities of expansion, researchers can more accurately assess the causal impact of Medicaid expansion on health outcomes using the design-based framework. This framework helps account for selection bias and provides a more robust analysis.