Public Policy|Public Policy and Administration|Quantitative Analysis

*This is a compulsory module for students registered on the Masters of Public Administration (MPA) programme and is not available as an optional module.

The...

Data Analysis for Public Policy (PUBL0062)

This module lifts the lid on the business of policy making, examining the various forms of evidence used in the process, and the dark arts of political...

Politics and Policy in Practice (POLS0064)

This module lifts the lid on the business of policy making, examining the various forms of evidence used in the process, and the dark arts of political communication.
The module is framed around a speaker series, where practitioners from various fields of public policy share their insights and experiences. In the past we've heard from (among others) a former director of political strategy in No.10; a communications manager at the Resolution Foundation; the chief scientific officer at the Behavioural Insights Team; the chairman of the Office for Budgetary Responsibility; and the communications director at the Ministry of Justice.

Human rights and global politics are intimately related. This module introduces students to this relationship by exploring some of the most complex and...

Human Rights and World Politics (POLS0027)

The module&#039;s main objective is to provide students with an introduction to the rapidly growing field of causal inference. Increasingly, social scientists...

Causal Analysis in Data Science (POLS0012)

The module's main objective is to provide students with an introduction to the rapidly growing field of causal inference. Increasingly, social scientists are no longer willing to establish correlations and merely assert that these patterns are causal. Instead, there is a new focus on design-based inference, designing research studies in advance so that they yield causal effects. This module discusses the nature of causation in the social sciences, and goes on to look at some of the most popular research designs in causal analysis, including experiments (also known as randomised control trials), natural experiments that we can analyse with instrumental variables and regression discontinuity techniques, as well as causal inference over time using the methods of difference-in-differences and synthetic control. We will also evaluate ‘observational’ methods -- regression and the closely related technique of matching -- from the standpoint of causal inference. This module has a hands-on, practical emphasis. Students will learn to design effective studies and implement these methods in R, and will become critical consumers and evaluators of cutting-edge research, able to read and evaluate original journal articles. Examples will be drawn from economics, political science, public health and public policy.
Prerequisites (for PIR who have completed POLS0083):
- be reasonably confident with R (e.g., you understand how R works, know how to load data, run regressions in R)
- be familiar with concepts and terms such as statistical and substantive significance, hypothesis tests, p-values, potential outcomes
- be comfortable interpreting regression coefficients
 