Evaluating Social Programs
Learn why and when randomized evaluations can be used to rigorously evaluate the impact of social programs and how findings can inform the design of evidence-based policies and programs.
Learn why and when randomized evaluations can be used to rigorously evaluate the impact of social programs and how findings can inform the design of evidence-based policies and programs.
At a Glance
More About the Course
This course explores each step in designing a randomized evaluation, from developing a theory of change and conducting the randomization process to navigating design challenges and ethical considerations. Through lectures led by J-PAL affiliated professors and case studies using real-world examples, you will gain an understanding of both technical design aspects and practical considerations for measuring impact with a randomized evaluation. Throughout the course, you will learn how to recognize opportunities for evaluation, how to design a high-quality randomized evaluation, and how to maximize policy impact and assess the generalizability of research findings.
This course is designed for policymakers, program implementers, and practitioners from governments, NGOs, international organizations, foundations, and beyond, as well as students looking for an introduction to randomized evaluations. Join a community of learners from around the world who are interested in learning how rigorous evaluation and evidence can ensure their organizations’ programs have the intended impact.
For researchers looking for more in-depth practical guidance for conducting randomized evaluations, including modules on survey design and data collection and management, we encourage you to enroll J-PAL’s semester-long course on Designing and Running Randomizations which is part of the MITx Data, Economics, and Development Policy MicroMasters Program.
Organizations -- contact the J-PAL Training Team to explore how to enroll your staff as a cohort in our blended learning program.
Although not required, prior familiarity with basic statistical concepts is recommended.