Several structural barriers make it difficult for government officials and academic researchers to engage collaboratively in evidence-based policymaking. These include a lack of common language for discussing programs, limited methodological strategies for establishing causation without randomized control trials (RCTs), and data-sharing restrictions. The Causal Inference for Social Impact Lab (CISIL) finds solutions to these barriers and enhances academic-government collaboration.
The Lab is led by 2018-19 CASBS fellow Jake Bowers, 2017-18 CASBS fellow Carrie Cihak, and CASBS program director Betsy Rajala.
CISIL has received funding from SAGE Publishing, the Knight Foundation, and the Alfred P. Sloan Foundation.
In Spring 2021, CASBS will formally launch the Causal Inference for Social Impact Lab’s Data Challenge. Approximately ten academic teams from a variety of disciplinary backgrounds will be invited to design and execute an analysis plan for one of two predetermined policy evaluation questions. Unlike most data challenges, the data are real, the answers from the teams will drive policy decisions, and teams are free to use any valid statistical technique they see fit to answer the questions posed to them.
CISIL will then collect the analysis plans for each team. These plans, and the decisions described within them, will then become the data CISIL uses to understand the cumulative effect that different -- but reasonable -- statistical choices have on an analysis. We will pay particular attention to decisions that affect the overall policy recommendations teams would have made.