Department of Mathematics and Statistics

Causal models

Contact person: Prof. Juha Karvanen

PhD students: Juho Kopra and Santtu Tikka

Collaborators: Prof. Olli Saarela (University of Toronto), Prof. Elias Bareinboim (Purdue University)

Related projects: Non-participation in health examination surveys

Creating and implementing algorithms for causal inference

Publications:

S. Tikka, J. Karvanen, Simplifying probabilistic expressions in causal inference, Journal of Machine Learning Research, 18(36), 1-30, 2017.

S. Tikka, J. Karvanen, Identifying causal effects with the R package causaleffect. Journal of Statistical Software, Volume 76, 2017.

Causal models with design

The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely.

Causal models with design describe the study design and the missing data mechanism together with the causal structure and allow the direct application of causal calculus and the concept of ignorability. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation.

selection_for_response_measurement.gif

Figure 1: Causal model with design for a study where the individuals are selected for the measurement of the response variable on the basis of the measured covariate variable.

Publications:

J. Karvanen, Study design in causal models. Scandinavian Journal of Statistics, Volume 42, Issue 2, pages 361-377, DOI: 10.1111/sjos.12110, 2015.