Causal Inference for Social Impact Lab
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.
For more information, please contact CASBS program director Betsy Rajala
The Causal Inference for Social Impact Lab (CISIL) at the Center for Advanced Study in the Behavioral Sciences (CASBS) invites applications from teams interested in participating in the CISIL data challenge.
- You will use real administrative data on transportation and demographics from King County (Seattle), Washington.
- The data challenge questions are generated by King County policymakers and are typical of the kinds of questions in evidence-inform3ed public policy evaluations that are growing in prevalence in the USA and around the world.
- Analyses from data challenge teams will be used by policymakers to advance transportation equity
- The data includes a non-randomized study for which there is no one known correct answer
Learn more or apply to participate