The Case for Randomised Trials (and Why Big Data Does Not Supersede Randomisation)

Abstract

Research Question/Issue

With the growing availability of large-scale datasets, is randomisation still necessary for identifying causal impacts?

Research Findings/Insights

Randomised trials, by using luck to assign participants to treatment and control groups, reliably provide a credible counterfactual that ensures observed differences reflect causal impacts. In contrast, observational data often produces misleading correlations that fail to replicate under experimental conditions. Therefore, the increased availability of big data does not make randomisation obsolete.

Practitioner/Policy Implications

I propose five approaches to increase the quality and quantity of randomised policy trials: encourage curiosity in yourself and those you lead; seek simple trials, especially at the outset; ensure experiments are ethically grounded; foster institutions that push people towards more rigorous evaluation; and collaborate internationally to share best practice and identify evidence gaps.

Methods Used

This paper employs a qualitative synthesis of historical and contemporary examples, illustrating the superiority of randomised trials over purely observational methods. By drawing comparisons across disciplines—economics, health, and social policy—it highlights how nonexperimental approaches can fall short and explores how big data can be a complement to rigorous randomised trials.