Resources

Online resources

Causal Diagrams by Miguel Hernán: https://www.edx.org/course/causal-diagrams-draw-assumptions-harvardx-ph559x

DAGitty: Drawing and analyzing causal diagrams (DAGs) by Johannes Textor:
http://www.dagitty.net/

Causal inference book by Miguel Hernán and James Robins:
https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Causal mediation analysis by Tyler VanderWeele:
https://www.hsph.harvard.edu/tyler-vanderweele/tools-and-tutorials/

Introduction to causal inference by Maya Petersen and Laura Balzer:
http://www.ucbbiostat.com/

Judea Pearl’s blog: http://causality.cs.ucla.edu/blog/

Selected estimation packages

Authors	Method	Software
Emsley & Liu	Regression	paramed (Stata)
Valeri & VanderWeele	Regression	mediation (SAS, SPSS)
Tingley et al.	Regression	mediation (R, Stata)
Daniel et al.	g-formula	gformula (Stata)
Lin et al.	g-formula	mgformula (SAS)
Steen et al.	Structural mean models	medflex (R)
Muthén & Asparouhov	SEM	Mplus

Overview articles (from different disciplines)

Mediation:

• 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.

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.

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.
• Sampson, R. J. (2010). Gold standard myths: Observations on the experimental turn in quantitative criminology. Journal of Quantitative Criminology 26 (4), pp. 489–500.

Causality bookshelf

Mediation:

• 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.

Introductions to causal inference:

• Hernán, M. A. and Robins, J. M. 2018. Causal Inference (v. 04-10-17). 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.
• 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.

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.
• Morgan, S. L. (ed.) 2013. Handbook of Causal Analysis for Social Research. Dordrecht: Springer.
• Pearl, J. and Mackenzie, D. 2018. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.

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.