Causal Inference in Quantitative Social Research (Intensive)

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects. This course will introduce graduate students to core issues about causal inference in quantitative social research, focusing especially on how one can move from demonstrating correlation to causation. The first lecture will define key concepts of correlates, risk factors, causes, mediators and moderators. The second lecture will discuss quasi-experimental research designs (studies without random assignment), and issues of “validity” in drawing causal conclusions. The third and fourth sessions will be lectures and practicals introducing two key analytic methods (propensity score matching and fixed effects regression models) that can be used to help identify causes. The course will focus on studies in which individual people are the basic unit of analyses, particularly longitudinal studies which follow the same people over multiple waves of assessment.

• Mphil and PhD students from participating departments taking the Social Science Research Methods Centre training programme as part of their research degree

• Basic familiarity with survey research methods and regression models.
• You must have a University Information Services (Computing) Desktop Services password (http://www.ucs.cam.ac.uk/linkpages/newcomers)
• You must have access to the associated Moodle course page (http://www.ssrmc.group.cam.ac.uk/ssrmc-modules/before-first-session)

Number of sessions: 2

• Key concepts, from correlates to causes
• Quasi-experimental Studies and Causal Inference
• Propensity Score Matching and Causal Inference
• Fixed-effects Regression Models and Causal Inference

• Kraemer, H. C., Lowe, K. K., & Kupfer, D. J. (2005). To Your Health: How to Understand What Research Tells Us About Risk. New York, NY: Oxford University Press.
• Kraemer, H. C., Kazdin, A. E., Offord, D., Kessler, R. C., Jensen, P. S., & Kupfer, D. J. (1997). Coming to terms with the terms of risk. Archives of General Psychiatry, 54(4), 337-343.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton Mifflin.
• Farrington, D. P. (2003). Methodological Quality Standards for Evaluation Research. Annals of the American Academy of Political and Social Science, 587, 49-68.
• Guo, S., & Fraser, M. W. (2010). Propensity Score Analysis: Statistical Methods and Applications. Thousand Oaks, CA: Sage.
• Williamson, E., Morley, R., Lucas, A., & Carpenter, J. (2012). Propensity scores: From naïve enthusiasm to intuitive understanding. Statistical Methods in Medical Research, 21(3), 273-293. doi: 10.1177/0962280210394483
• Allison, P. (2009) Fixed Effects Regression Models. London: SAGE. (Though the Brüderl paper below should suffice).
• Brüderl J. (2005) Panel Data Analysis. http://www.sowi.uni-mannheim.de/lehrstuehle/lessm/veranst/Panelanalyse.pdf

• To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
• Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.

• This is an intensive, one-day module