Methods for causal inference in educational research, 7.5 credits
The course aims at developing participants’ skills to choose and apply appropriate techniques for answering causal questions on the basis of observational data, as well as to critically review educational research which aims to make causal inferences. Some examples of such techniques are instrumental variable regression (IV), regression discontinuity design (RDD), differences-in-differences (DID or DD), and propensity score matching (PSM).
In the first part of the course the distinction between causal and non-causal research questions is introduced, and the reasons why it is generally impossible to answer causal questions through analyzing associations among observed variables are made explicit.
In the second part of the course three frequently used approaches for answering causal questions are treated in some detail, namely regression-discontinuity (RD) designs, IV regression analysis and fixed-effects/difference-in-differences. The logic upon which these approaches is based is presented, and the use and interpretation of the techniques is illustrated with both simulated and real data in the R system.
In the third part of the course structural equation modelling and propensity score matching are presented as methods which use conditioning on observed and latent variables to prevent bias in estimates of causal effects.
The fourth and final part of the course demonstrates how RD and IV designs may be combined, and how IV models may be estimated with SEM techniques.
Entry requirements
Knowledge equivalent to the learning goals in QRMs courses Regression analysis in educational research (QRM1802) and Structural equation modelling in educational research (QRM1806).
Course code
QRM1809
Application period
Fall 2024: 2024-04-01 - 2024-06-02