Concepts of Causality
in Biology and Medicine

Summer term 2007

Overview:

In this seminar we discuss some of the statistical and mathematical foundations of causality and causal modeling, with applications in biology and medicine. Causality is an important topic not only in all fields of complex systems studies (such as systems biology or econometrics/-physics) but also in medicine (e.g. clinical studies).

The seminar is intended for advanced students/researchers in mathematics, statistics, physics, and computer science. It requires some a priori knowledge of statistics and probability theory.

Reading List and Topics:

Please follow the links below to obtain a pdf copy of the paper (requires the appropriate licenses). The papers may also be obtained directly from the organizers, or from the internal IMISE paper archive.

Download reading list as text file.

1. Overview and Introduction:
   • Aalen, O. O. and A. Frigessi. 2007. What can statistics contribute to a causal understanding? Scand. J. Statist. 34: 155-168.
   • Frosini, B. V. 2006. Causality and causal models: a conceptual perspective. Intl. Statist. Review 74: 305-334.
   • Pearl, J. 2003. Statistics and causal inference: a review. TEST 12: 281-345.
2. Graphical Models:
   • Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82: 669-710
   • Steyvers, M., et al. 2003. Inferring causal networks from observations and interventions. Cognitive Science 27:453-489.
   • Shimizu, S., et al. 2006. A linear non-Gaussian acyclic model for causal discovery. JMLR 7: 2003-2030.
   • Eaton, D., and K. Murphy. 2007. Exact Bayesian structure learning from uncertain interventions. AISTATS 2007.
3. Potential Outcomes Theory:
   • Rubin, D. R. 2004. Direct and indirect causal effects via potential outcomes. Scand. J. Statist. 31: 161-170.
   • Rubin, D. R. 2005. Causal inference using potential outcomes: design, modeling, decisions. JASA 100: 322-331.
   • Tan, Z. 2006. A Distributional approach for causal inference using propensity scores. JASA 101: 1619-1637.
4. Miscellaneous:
   • Cox, D.R., and Wermuth, N. 2004. Causality: a statistical view. Intl. Statist. Review 72: 285-305.
   • Arjas, E., and J. Parner. Causal reasoning from longitudinal data. Scand. J. Statist. 31: 171-187.

Schedule:

The first meeting will take place on 17 April 2007 (assignment of talks). The following Tuesday (24 April) there is a causality workshop at MPI MiS that participants of the seminar are invited to attend. The introductory talks then start 8 May 2007 (as May 1 is public holiday).

Further Reading:

As background to the above papers we suggesting reading the following books:

1. Pearl, J. 2000. Causality. Cambridge University Press.
2. Rubin, D. 2007. Matched sampling for causal effects. Cambridge University Press.
3. Shipley, B. 2002. Cause and correlation in biology. Cambridge University Press.
4. Spirtes, P., C. Glymour, and R. Scheines. 2001. Causation, Prediction, and Search. 2nd edition. MIT Press.
5. Cox, D.R., and N. Wermuth. 1996. Multivariate Dependencies. Chapman & Hall

Last modified:
April 16, 2007