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Rapid-cycle evaluation uses a rigorous, scientific approach to provide decision makers with timely and actionable evidence of whether operational changes improve program outcomes. Often, changes can be tested in a matter of months, and decision makers can have a high degree of confidence in the results. Rapid-cycle evaluation can also help avoid investments in changes that are unlikely to produce the desired results.

Mathematica has been a leader in helping clients employ this short-term, low-cost alternative to traditional program evaluation, which the White House Office of Management & Budget’s Evidence and Innovation Agenda Memo of July 2013 recommended as a way to deliver a “smarter, more innovative and more accountable government.” Rapid-cycle evaluation can provide evidence of what works, resulting in savings of money, time, and resources, as well as better outcomes for people. Read about some of our projects.

Explore some of our work in rapid-cycle evaluation:

  • The Growing Demand for Rapid-Cycle Assessment—What Works Best and When? This forum examined approaches to rigorous rapid-cycle assessment, including opportunities and challenges, particularly when assessing interventions that may not show immediate results.

  • Smarter, Better, Faster: The Potential for Predictive Analytics and Rapid-Cycle Evaluation to Improve Program Development and Outcomes. This white paper explores the promise of predictive modeling and rapid-cycle evaluation— both individually and together—to improve programs in an increasingly fast-paced policy and political environment. 

  • Administrative Experiments: Unlocking What Works Better and What Works for Whom. This article provides an overview of rapid cycle evaluation, describes its use in identifying what works best for whom, and provides an illustrative example of how the techniques could be applied to the veterans’ employment services area.

  • The Transition to Civilian Life: Testing Program Changes to Boost Veteran Employment. This issue brief discusses how to use rapid-cycle evaluation to assess modest changes to re-employment programs that serve veterans and to quickly determine whether the changes truly enhance the programs.

  • What Works for Whom? A Bayesian Approach to Channeling Big Data Streams for Policy Analysis. In the coming years, public programs will continuously capture even more and richer data than they do now, including data from web-based tools used by participants in employment services, from tablet-based educational curricula, and from electronic health records for Medicaid beneficiaries. This paper presents a Bayesian approach to randomized policy evaluations that efficiently estimates heterogeneous treatment effects, identifying what works for whom.