Dara Kay Cohen Photo

Dara Kay Cohen

Professor of Public Policy

HKS shield.

Tim Layton

30th Anniversary Associate Professor of Health Care Policy, HMS

Liz McKenna Photo

Liz McKenna

Assistant Professor of Public Policy
Benjamin Schneer Photo

Benjamin Schneer

Assistant Professor of Public Policy

Daniel Schneider Photo

Daniel Schneider

Malcolm Wiener Professor of Social Policy, HKS; Professor of Sociology, FAS


This course is a 6-week module that follows on API-202M, allowing students to further specialize in a particular area of evidence for policy making. First year MPP students can select among the following four options. 

Section A (Benjamin Schneer) Causal Inference and Prediction: This module will delve deeper into quantitatively evaluating the impact of programs through quasi-experimental study designs, and assessing the strengths and weaknesses of different approaches. Students will also be introduced to the language and techniques of prediction and machine learning. Analyses of datasets will be conducted using R.

Section B (Dara Kay Cohen) and Section C (Liz McKenna) Qualitative Methods: This module will cover basic principles and particular techniques of qualitative data collection and analysis through experiential, applied learning on a policy puzzle of students’ choosing. Research methods will focus primarily on interviewing and ethnographic observation. Students will learn the skills of qualitative coding and analysis as well as how to evaluate and make use of qualitative evidence in policy analysis. Topics will cover research ethics, and how qualitative research is used in policy analysis and multi-method research.

Section D (Daniel Schneider) Survey Methods: Survey research is used in a variety of fields to create original data and answer questions that otherwise couldn’t be answered. This module is designed to teach students the skills necessary to create, use, and interpret survey data. The module will cover questionnaire design, sampling, modes of data collection, and other topics. Students will complete applied assignments in survey methods.

Section Z (Timothy Layton):  Similar to Section A, this module will delve into quasi-experimental techniques most commonly used for causal inference, laying out the mathematical foundations for each approach, and assessing the underlying mathematical assumptions required for each method to produce causal estimates. Students will also be exposed to modern methods for prediction and machine learning, with a detailed description of the statistical theory behind these approaches along with instruction in their practical application. All analysis will be conducted using R.  Please note, students in Section Z of API-202M will continue on to Section Z of API-203M.

Please note, API-203M is offered February 26 - April 12, 2024.