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Michela Carlana

Assistant Professor of Public Policy

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Tim Layton

30th Anniversary Associate Professor of Health Care Policy, HMS

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Benjamin Schneer

Assistant Professor of Public Policy

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Teddy Svoronos

Lecturer in Public Policy


Intended as a continuation of API-201, this 5-week module equips students with an understanding of common tools of quantitative empirical analysis in policy applications. Much of the learning will take place through hands-on analysis of data sets, as well as case studies illustrating critical concepts. The course will cover describing relationships between multiple variables, handling data in the real world, the challenges surrounding making causal claims, and interpreting the results of studies in a way that is both technically sound and policy relevant. The module will focus primarily on the tool of regression analysis and deepen students' facility with the programming language R. 

Please note, API-202M is offered January 22 – February 23, 2024.

Prerequisite: API-201 or equivalent. May not be taken for credit with API-210. Please note, students in Section Z of API-202M will continue on to Section Z of API-203M.