This module is compulsory and will be taught in term B. The module builds on statistical methods covered in the Data Analytics module in term A.
The aim of the module is to introduce students to the problem of causal inference, to theories of how causality is established and to empirical methods used to identify causal effects. The main focus will be on randomized controlled trials and settings that are similar. Students will learn about different econometric techniques used to identify causal effects and will develop an understanding of the strengths and weaknesses of these effects. Students will also learn how to collect and organize data that comes from real or natural experiments, to analyze such data and to report on their results in ways that are accessible to non-specialists.
25% practical, 25% practical and 50% coursework
Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell. Causal inference in statistics: a primer. John Wiley & Sons, 2016.
Imbens, Guido W., and Donald B. Rubin. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, 2015.
Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2008.
Deaton, Angus, and Nancy Cartwright. Understanding and misunderstanding randomized controlled trials. No. w22595. National Bureau of Economic Research, 2016.