CAWS 2025 Summer Course

Causal Analysis Workshop Series 2025 Summer Course

This five-day summer school introduces causal discovery methods and their applications. It is taught at a level appropriate for someone with experience doing data analysis at a graduate student level or above. The course also includes a special guest lecture by Dominik Janzing, Principal Research Scientist at Amazon Research.

Causal discovery analysis uses a data-driven approach to identify the best-fitting causal structural model for your data. The impact of these methods on science is rapidly accelerating. Some examples of recent publications where causal discovery analysis played a prominent role are:

Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data

Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs

Causal Determinants of Postoperative Length of Stay in Cardiac Surgery using Causal Graphical Learning

Quantifying heterogeneity in mood–alcohol relationships with idiographic causal models

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

Over the course of this summer school, we will cover the assumptions, limitations, and uses of various causal discovery analysis methods. We will also discuss more advanced topics depending on student interests. Finally, students will coordinate with the summer school organizers to develop an individual project based on their own research interests.

For more details about the kind of content likely to be covered in 2025, see the previous CAWS2024 schedule.

Registering for this will include: an invitation to the online lectures, personalized guidance on your causal discovery project.