Fundamentals of Causal Learning
The toolkit of modern Data Scientists contains a large variety of instruments, both generic and specialized. All these tools can be used to analyze and extract useful connections from digital data. The theory behind guarantees that the resulting patterns will reflect the existing correlations, however not causal relationships. At the same time, in many applications, the desired outcome is the cause-effect model.
This course aims to discuss the conditions under which the correlation does imply causation and to present the research direction that studies these conditions in a general form – Causal Learning. We believe that the knowledge of the basics of Causal Learning is an indispensable element of a Data Science practitioner toolkit.
- Jupyter Notebooks
- Python programming
- Fundamental of statistics, probability, calculus.
Level of complexity of course
Dr. Marharyta Aleksandrova
After completing a Master’s degree from the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Marharyta started her research path by pursuing a joint doctorate program between France (University of Lorraine) and Ukraine (NTUU “Igor Sikorsky KPI”).
Currently, she holds the position of postdoctoral researcher at the University of Luxembourg. During these years, she has been exposed to various applications of Data Science and worked on several European and national research projects.
Fields of interests: Data analysis, Recommender systems, Application of Data Mining to security, Theory of machine learning (causal learning, conformal learning)
Contacts: [email protected]