There is a growing expectation from governmental, and non-governmental organisations for social scientists to produce research which are capable of assessing the impact of certain changes to policies or the emerging effects of interventions. Yet, it is often difficult to analyse such changes when traditional experiments cannot be carried out either on the grounds of feasibility or due to ethical constraints. This course will provide participants with a toolkit to analyse so-called 'quasi-experimental' designs and draw causal conclusions using observational datasets.

 

We have put together a course focused on addressing practical considerations including when certain designs can be credibly employed and how the emerging results can be interpreted. In particular, the five most commonly used families of methods will be discussed: matching, difference-in-differences, instrumental variables, causal mediation, and regression discontinuity designs. We are to demonstrate each of these approaches by discussing existing applications from across the fields of the social sciences. We will also provide a tutorial for the 'R' statistical software and share with the participants the code for the methods covered by the course. We are also going to provide ample opportunity for participants to discuss their research plans, ask for advice regarding their own data, and recommend cutting-edge methods to address their research questions.

By the end of the course participants will: