Causal Inference in Quantitative Social Research (Intensive)

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.

Topics:

• Key concepts, from correlates to causes
• Overview of quasi-experimental methods
• Propensity Score Matching

Note: this module was originally advertised as also covering fixed-effects regression models. Fixed-effects models have now been dropped from the content; students wishing to learn about them should attend the SSRMC module on panel data methods https://www.training.cam.ac.uk/jsss/event/2141519

• Basic familiarity with survey research methods and regression models.

Number of sessions: 2

• 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

Online multiple-choice test

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