Resources

Materials and homework submission on Ilias
https://www.ilias.uni-koeln.de/ilias/goto_uk_crs_2629351.html

Q&A on Piazza
This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates and myself. Rather than emailing questions, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.

Find our class page at:
https://piazza.com/uni-koeln.de/winter2018/basismodulsoziologieiii/home

Piazza is also available as a costfree app for Android and iOS.

DAGitty
Software to draw and analyze graphical causal models (DAGs) by Johannes Textor: http://www.dagitty.net/

Tutorial on using DAGitty by Scott Venners (video)

Other online resources

• Causal Diagrams by Miguel Hernán (free online class)
• Causal Inference Bootcamp (videos)
• Causal mediation analysis by Tyler VanderWeele (videos)
• Tutorial on non-parametric causal models by Thomas Richardson (video)
• Introduction to causal inference by Maya Petersen and Laura Balzer (course materials)
• Judea Pearl’s blog
• Twitter #Causalinference (selection):
  Laura Balzer, Elias Bareinboim, Rhian Daniel, Fernando Martel García, Maria Glymour, Miguel Hernán, Stephen L. Morgan, Ellie Murray, Manjari Narayan, Judea Pearl, Sherri Rose, Bianca De Stavola, Peter Tennant, Johannes Textor

Overview articles (from different disciplines)

Causal inference:

• Gangl, M. (2010). Causal inference in sociological research. Annual Review of Sociology 36, pp. 21– 47.
• Imbens, G. W. and Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47 (1), pp. 5–86.
• Keele, L. (2015). The statistics of causal inference: A view from political methodology. Political Analysis 23 (3), pp. 313–335.
• Petersen, M. L. and Laan, M. J. van der (2014). Causal models and learning from data: Integrating causal modeling and statistical estimation. Epidemiology 25 (3), pp. 418–426.
• Shalizi, C. (2018). Advanced data analysis from an elementary point of view. New York: Cambridge University Press, Ch. 21-24.

Graphical causal models:

• Elwert, F. (2013). Graphical causal models. In: Handbook of Causal Analysis for Social Research. Ed. by Morgan, S. L. New York: Springer, pp. 245–272.
• Glymour, M. M. and Greenland, S. (2008). Causal diagrams. In: Modern Epidemiology. Third Edition. Ed. by Rothman, K. J., Greenland, S., and Lash, T. L. Philadelphia, PA: Lippincott Williams & Wilkins, pp. 183–209.
• Rohrer, J. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science 1 (1), pp. 27-42.
• Steiner, P. M. et al. (2017). Graphical models for quasi-experimental designs. Sociological Methods & Research 46 (2), pp. 155–188.

Randomized controlled trials:

• Deaton, A. and Cartwright, N. (2016). Understanding and misunderstanding randomized controlled trials. Social Science & Medicine.
• Jackson, M. and Cox, D. R. (2013). The principles of experimental design and their application in sociology. Annual Review of Sociology 39, pp. 27–49.
• Mansournia, M. A., Higgins, J. P. T., Sterne, J. A. C., and Hernán, M. A. (2017). Biases in randomized trials: A conversation between trialists and epidemiologists. Epidemiology 28 (1), pp. 54–59
• Sampson, R. J. (2010). Gold standard myths: Observations on the experimental turn in quantitative criminology. Journal of Quantitative Criminology 26 (4), pp. 489–500.

Causal mediation analysis:

• Green, D. P., Ha, S. E., and Bullock, J. G. (2010). Enough already about “black box” experiments: Studying mediation is more difficult than most scholars suppose. Annals of the American Academy of Political and Social Science 628 (1), pp. 200-208.
• Keele, L. (2015). Causal mediation analysis: Warning! Assumptions ahead. American Journal of Evaluation 36 (4), pp. 500-513.
• Knight, C. and Winship, C. (2013). The causal implications of mechanistic thinking: Identification using directed acyclic graphs (DAGs). In: Handbook of Causal Analysis for Social Research. Ed. by Morgan, S. L. Dordrecht u.a.: Springer, pp. 275-299.
• VanderWeele, T. J. (2016). Mediation analysis: A practitioner’s guide. Annual Review of Public Health. 37, pp. 17-32.

Causality bookshelf

Introductions to causal inference:

• Hernán, M. A. and Robins, J. M. 2018. Causal Inference. Boca Raton, FL: Chapman & Hall/CRC.
• Imbens, G. W. and Rubin, D. B. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences: An Introduction. New York: Cambridge University Press.
• Morgan, S. L. and Winship, C. 2015. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Second Edition. New York: Cambridge University Press.
• Pearl, J., Glymour, M., and Jewell, N. P. 2016. Causal Inference in Statistics: A Primer. West Sussex, UK: Wiley.
• Pearl, J. and Mackenzie, D. 2018. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.
• Rosenbaum, P. R. 2017. Observation and Experiment: An Introduction to Causal Inference. Cambridge, MA: Harvard University Press.

Graphical causal models:

• Glymour, C. 2001. The Mind’s Arrows: Bayes Nets and Graphical Causal Models in Psychology. Cambridge, MA: MIT Press.
• Pearl, J. 2009[2000]. Causality: Models, Reasoning, and Inference. Second Edition. New York: Cambridge University Press.
• Sloman, S. 2005. Causal Models: How People Think About the World and its Alternatives. Oxford, UK: Oxford University Press.
• Spirtes, P., Glymour, C., and Scheines, R. 2001[1993]. Causation, Prediction, and Search. Second Edition. Cambridge, MA: MIT Press.

Causal mediation (and interaction) analysis:

• Hong, G. 2015. Causality in a Social World: Moderation, Mediation, and Spill-Over. West Sussex, UK: Wiley- Blackwell.
• VanderWeele, T. J. 2015. Explanation in Causal Inference: Methods for Mediation and Interaction. New York: Oxford University Press.

Causality:

• Berzuini, C., Dawid, P., and Bernardinelli, L. (ed.) 2012. Causality: Statistical Perspectives and Applications. West Sussex, UK: Wiley.
• Cartwright, N. 2007. Hunting Causes and Using Them: Approaches in Philosophy and Economics. New York: Cambridge University Press.
• Illari, P. and Russo, F. 2014. Causality: Philosophical Theory Meets Scientific Practice. New York: Oxford University Press.

Research design and methods:

• Angrist, J. D. and Pischke, J.-S. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ: Princeton University Press.
• Angrist, J. D. and Pischke, J.-S. 2015. Mastering ’Metrics. The Path from Cause to Effect. Princeton, NJ: Princeton University Press.
• King, G., Keone, R., and Verba, S. 1994. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton,NJ: Princeton University Press.
• Manski, C. F. 1995. Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press.
• Manski, C. F. 2007. Identification for Prediction and Decision. Cambridge, MA: Harvard University Press.
• Murnane, R. J. and Willett, J. B. 2010. Methods Matter: Improving Causal Inference in Educational and Social Science Research. Oxford University Press.
• Peters, J., Janzing, D., and Schölkopf, B. 2017. Elements of Causal Inference: Foundations and Algorithms. Cambridge, MA: MIT Press.
• Rosenbaum, P. R. 2010. Design of Observational Studies. New York: Springer.
• Shadish, W., Cook, T., and Campbell, D. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Belmont, CA: Wadsworth Cengage Learning.
• van der Laan, M. and Rose, S. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. New York: Springer.