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Tue 22 Jan 2019
14:00 - 16:00

Venue: 8 Mill Lane, Lecture Room 6

Provided by: Social Sciences Research Methods Programme


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Other dates:

Wed 4 Mar 2020

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Causal Inference in the Social Sciences

Tue 22 Jan 2019


The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects. This introductory session will:

(i) set out some basic barriers to causal inference in the social sciences and why this matters;
(ii) describe the counterfactual framework that underpins much of the discussion of causal inference;
(iii) talk through the intuition of several research designs that can help researchers make stronger claims for causal relationships.

The emphasis is on setting out applications of each approach, along with pros and cons, so that participants understand when a particular design may be more or less suitable to a research problem.

  • Basic familiarity with survey research methods and regression models.

Number of sessions: 1

# Date Time Venue Trainer
1 Tue 22 Jan   14:00 - 16:00 14:00 - 16:00 8 Mill Lane, Lecture Room 6 map Alex Sutherland
  • 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.
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