While experimental evaluations have many merits, greatly expanding their scope and rigor also poses significant challenges that must be addressed. One major challenge is that true randomized controlled experiments are often difficult, costly, or unethical to implement on a large scale across many programs and policies. Certain programs are simply not amenable to control groups due to ethical or practical constraints. For example, it would not be feasible or appropriate to randomly assign some students to for-profit colleges while denying others the opportunity in order to evaluate impacts.
Relatedly, the desire for more rigorous evaluation often conflicts with real-world constraints around program design and rollout. Politicians and program administrators face pressures to launch new initiatives quickly to address pressing issues. This limits the ability to first design programs specifically to facilitate evaluation or to take the time needed to pilot and refine interventions before broader implementation. The reality is that most programs are not created primarily for research purposes. Retrofitting them later for more rigorous evaluation is challenging.
Expanding experimental evaluation substantially raises data demands. Large-scale randomized experiments require collecting extensive individual-level data over long periods on both program participants and control groups, as well as cleaning, linking, and analyzing massive datasets. This type of data infrastructure is costly to create, maintain over time, and gain approval to access for research purposes due to confidentiality concerns. Related privacy and ethical issues also arise around collecting, storing and sharing sensitive personal information on a wide scale.
There are also concerns about demand characteristics, coercion, and unintended behavioral responses in experimental designs when study populations realize they are part of an evaluation. Simply evaluating more programs more rigorously could potentially influence the nature and quality of service delivery. Staff may feel pressure to artificially boost measured outcomes, for example. Also, participants assigned to a control group aware they are not receiving a promoted service could behave differently than they otherwise would.
The generalizability of even very rigorously-evaluated programs also remains limited by contextual factors not captured in experiments. Results obtained from evaluating a given policy under specific conditions may not translate predictably if the same policy is implemented differently elsewhere with varying target populations, available resources, community characteristics, and so on. Likewise, evaluations focus on discrete policies or interventions but the impacts of any given program are often confounded by simultaneous changes in the broader environment over time. Sorting out the influence of contextual factors poses methodological challenges.
Calls to vastly scale up randomized experimental evaluations could paradoxically reduce their credibility and influence if not implemented judiciously. Done poorly or without constraint, “evaluation for evaluation’s sake” risks producing a mountain of low-quality, inconclusive results that policymakers rightly learn to ignore or discount. Experimental evaluations demand substantial expertise and resources to design well, avoid biases, and yield clear, robust findings – qualities that become rarer as the volume of evaluations grows without regard to proportional increases in funding and methodological support. There is also a risk of “diluting the brand” of experimental methods through low-quality imitations that undermine trust in the approach.
Substantially increasing both the scope and rigor of impact evaluations faces major obstacles around the logistical and ethical feasibility of implementing randomized controlled trials at scale across diverse policy contexts, as well as gaps in data infrastructure, unintended behavioral consequences of evaluation designs, limited generalizability, and the very real risk of diminishing returns from vastly expanding evaluation activity without commensurate safeguards for quality. If the goal is to generate sound evidence that directly informs real-world policy and practice, these challenges must be addressed systemically through coordinated long-term investments in methodology, capacity-building, and innovation.