Evaluating Generative AI Through Quasi-Experimental Design
Contextual Awareness is All You Need Post #7: Dr. Rumman Chowdhury
How do we understand the impact of a system-level decision that could completely shift educational outcomes, or market dynamics, or health outcomes? These are the questions currently being asked of AI systems. Fortunately, these are also the same questions that have been asked of the social sciences for decades when evaluating everything from federal policy shifts to funding for school bussing. Quasi-experimental design, long used for evaluating broad impacts of scaled policy shifts, offers such a framework, enabling robust, ethical, and scalable evaluation of AI interventions.
Through the course of this series, we’ve discussed how existing evaluation practices have significant limitations when applied to advanced systems operating in complex, real-world environments. Common methods can fail to capture the broader social, behavioral, and systemic consequences of deploying generative AI at scale. As AI systems exercise increasing influence in public discourse, labor markets, and decision-making, an expanded evaluation framework is needed—one capable of identifying both intended and unintended outcomes.
At present, mainstream evaluation frameworks for AI rely heavily on performance metrics such as accuracy, recall, and F1 scores applied to curated and often static datasets. These metrics can provide meaningful technical signals, but their reach is sharply constrained. Performance on curated benchmarks frequently fails to generalize to open or adversarial settings. Moreover, centrally maintained benchmarks may unintentionally shape development priorities and lead to overfitting, making models appear more capable than they truly are when deployed. Red-teaming—a complementary evaluation method gaining attention for its role in surfacing vulnerabilities and emergent threats—offers benefits but tends to be labor-intensive, inconsistent across organizations, and difficult to replicate. Furthermore, few current evaluation practices adequately address downstream effects, such as compositional bias, user manipulation, systemic destabilization, or external misuse. At the root of these limitations is a broader methodological gap: the lack of rigorous causal inference tools adaptable to real-world deployment scenarios.
This is where quasi-experimental design becomes useful. It refers to a family of empirical research methods that aim to infer causal effects without the benefit of randomized treatment assignment. Unlike controlled experiments, where units are randomly assigned to treatment or control groups, quasi-experiments work with non-randomized data, often using natural settings or operational constraints as sources of variation. While this introduces certain limitations in internal validity, these designs can approximate experimental conditions when structured carefully, especially if paired with strong domain knowledge and robust statistical techniques.
The distinguishing feature of quasi-experimental methods is their reliance on observed, rather than assigned, variation in treatment exposure. For instance, if two populations are very similar but only one receives an AI intervention due to a policy or rollout constraint, a comparative analysis across these populations could yield useful inferences about that intervention’s effects. Techniques such as propensity score matching, difference-in-differences estimation, and regression discontinuity analysis help mitigate selection bias and confounding variables when randomization is not feasible. Quasi-experimental studies often employ both cross-sectional and time-series data, allowing researchers to assess causal effects across multiple levels of influence—from individual users to institutional systems.
There are several advantages to quasi-experimental approaches in AI evaluation. Most importantly, they can be conducted in real-world settings without requiring randomized treatment, which is often ethically or operationally infeasible. For instance, deliberately withholding a potentially beneficial AI service from a randomly chosen group of users would likely raise objections from both institutional review boards and affected communities. Quasi-experimental designs, by contrast, align more naturally with the actual process of software deployment, where staggered rollouts and policy variations occur regularly. Because these methods draw on real-world exposure and authentic user behavior, they tend to yield findings with greater ecological validity than artificially constructed lab tests. Additionally, quasi-experiments are more scalable and cost-effective than randomized control trials, particularly when working with systems already embedded in digital infrastructures.
Nevertheless, quasi-experimental evaluation is not without its challenges. The lack of randomization makes these methods more susceptible to confounding factors—especially unmeasured group-level differences. Causal inferences must therefore be interpreted cautiously, and validation typically requires extensive robustness checks. This includes sensitivity analyses, placebo tests, and the use of multiple comparison groups. Analysts must also ensure that the "parallel trends" assumption holds—i.e., that in the absence of treatment, treated and control groups would have evolved similarly over time. In many cases this assumption can only be partially verified. Analysts must complement statistical rigor with careful design choices informed by domain, deployment processes, and operational context.
The applicability of quasi-experimental frameworks to generative AI is particularly compelling given the way these systems tend to be deployed. AI features are often phased in gradually, either because of resource constraints, regulatory requirements, or business strategies. These staggered or selective rollouts, while rarely structured for the purpose of research, inadvertently create evaluation opportunities if the affected populations can be cleanly compared. For instance, if an AI-assisted legal writing tool is deployed to only some lawyers at a firm, or a generative moderation system is tested in just a few regional markets of a global platform, these circumstances can be analyzed for variation in outcomes across similar groups.
Consider a more specific example involving the phased deployment of a generative language model for automated content moderation on a social media platform. Due to computational capacity limits, the model is introduced first in a subset of regions while other areas continue to rely on traditional rule-based moderation. Evaluators begin by establishing baseline metrics for both sets of regions—monitoring variables such as moderation latency, user satisfaction, policy violation rates, and complaint submission volumes. After the intervention, these same metrics are collected again over an equivalent time frame.
The next step might involve difference-in-differences analysis, comparing the shift in outcomes in treated regions against the same metric shifts in untreated regions over the same period. If the differences are statistically significant and aligned with the temporal structure of intervention, they can suggest an effect attributable to the AI deployment. To refine the analysis, evaluators could apply matching on key regional attributes such as user base size, historical enforcement patterns, or content type distribution, helping to equalize the two groups and limit confounding. Follow-up analyses could then differentiate between short-term and persistent effects, disaggregate results by content category, or evaluate whether the generative system introduced its own unintended harms—such as increased false positives, user disengagement, or new attack surfaces.
While this kind of study cannot produce definitive claims of causality in the way a randomized trial might, it does provide strong directional evidence—particularly if validated with additional robustness checks and sensitivity analyses. Importantly, this approach aligns with the realities of platform operations and deployment schedules. It leverages structure that already exists, without requiring intrusive or manipulative research protocols, and it supports decision-making grounded in concrete behavioral outcomes.
Quasi-experimental evaluations can also support broader objectives in AI governance, particularly when multiple stakeholder groups are involved. For governments overseeing public procurement of AI systems, understanding real-world effects—rather than laboratory approximations—is crucial. Similarly, internal ethics review boards within technology companies can benefit from these methods when weighing risks and benefits of incremental feature releases. In both cases, quasi-experimental frameworks help close the gap between theoretical compliance and empirical accountability.
As AI systems continue to mediate high-stakes decision processes and everyday user interactions, an expanded methodological toolkit is required to evaluate their consequences thoroughly and reliably. Quasi-experimental design offers a pragmatic yet analytically sound alternative to randomized evaluation, allowing researchers and practitioners to assess system performance, generalizability, and externalities using data from actual deployments. As the AI field matures, embedding these methods into model evaluation protocols will be essential for ensuring that technological claims are matched by real-world impact—and that social, ethical, and epistemic concerns are addressed with the rigor they deserve.