This module will develop students’ understanding of quantitative analysis and causal This is an intensive module on causal inference and advanced statistical modelling, providing students with the techniques to critique methods used in contemporary academic and policy work. It begins with an overview of causality in the social sciences, covering the fundamental problem of causal inference and randomized experiments (RCTs). The second part of the course delves into the most widely used approaches for drawing causal inferences from quasi-experimental data, such as difference-in-differences and regression discontinuity designs. Each week, a one-hour lecture will be followed by a two-hour lab-based session, where students will use R to implement the methods covered in the lectures.
Due to the advanced research methods involved in this module, some prior experience of quantitative research methods is required. SC504-7-AU would be the most relevant module to take alongside this module.
Module Aims:
The aims of this module are:
· To get familiar with the concept of causality.
· To understand of the motivation and theoretical underpinnings of common counterfactual designs.
· To gain an overview of common tools for causal analysis.
· To understand the strengths and weaknesses of these tools for answering specific research questions.
· To learn to use these tools in R.
· To develop skills to critically discuss findings and their interpretations from policy reports and academic research relevant to policing, criminology, and sociology.
- Module Supervisor: Sergio Lo Iacono