Causal Inference
A hacking day of Mathematics for Data Science at the Department of Mathematics
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Bio
Professor Ernst C. Wit is Professor of Statistics and Data Science at the Università della Svizzera italiana in Lugano, Switzerland. He obtained PhDs in Philosophy (1997, Penn State) and Statistics (2000, University of Chicago) in the US. From 2000 until 2005 he was in the Statistics Department at the University of Glasgow, where he became a Reader. In 2005 he became head of the Medical Statistics Unit (12 FTE) at the University of Lancaster as full professor. As Director he implemented a thriving Master in Statistics programme. From 2008 until 2018 Wit was at the University of Groningen, for the last 4 years as head of the Mathematics Department. Since 2018 he is in Switzerland, where he has continued to work on methodological development in high-dimensional inference with a specific focus on network modelling. He is the author of 120 peer-reviewed publications. He has served as the President of the European Bernoulli Society and as member of the Board of Directors of the International Biometrics Society. He was president of the Dutch Biostatistics Society. From 2016 until 2020 he presided over a European COST Action, entitled COSTNET (CA15109), that dealt with novel methods for statistical network science and that brought together 34 countries and some 500 researchers throughout Europe. He is a founding member of the Data Science and Systems Complexity Center (DSSC), which also established a Data Science Master and a Statistics and Big Data Master, at the University of Groningen. Wit advises the Ministry of Internal Affairs in the Netherlands on statistical matters relating to elections and referendums since 2014. Wit is currently the Director of the Institute of Computing at USI and the director of the Master in Computational Science.Course description
This course is an introduction to causal inference and is embedded within the teaching of the course “Graphical models and network science” of the LM in Mathematics. Our point of departure are causal graphical models, where causality is interpreted in terms of the effect of an intervention. The main question will be whether and how counterfactual information can be inferred from factual observed data. The course will explore both traditional methods for causal discovery, most notably the PC algorithm, and modern approaches for large systems based on regularized inference.
No prior experience with causality is expected, but familiarity with statistical modeling, and in particular graphical modelling, is essential.
Course objectives
By the end of the course, the students will be able to:
- Understand causality in the context of graphical models
- Use traditional and modern approaches for causal discovery
Textbooks
- The course will mainly use the notes written by the instructor.
- Useful text on the side: S. Lauritzen (1996) Graphical models.
Computation
For the practical part of the course, we will use R. R can be downloaded for free from cran.r-project.org. Furthermore, a lot of students find it handy to interface R with R-studio, which again can be downloaded for free from: www.rstudio.com.
Schedule
- Thursday 11 November 2021 @ 09.30-11.30 Causal graphical models (via Zoom)
- Tuesday 16 November 2021 @ 11.30-12.30 Causal graphical models in R (via Zoom)
- Thursday 18 November 2021 @ 09.30-11.30 Causal discovery (via Zoom)
- Friday 19 November 2021 @ 14.30-15.30 Causal discovery in R (via Zoom)
- Thursday 25 November 2021 @ 09.30-11.30 Causal regularization (Aula A219, Povo 1)
- Friday 26 November 2021 @ 13.30-14.30 Causal regularization in R (Aula A209, Povo 1)
Details
- Participation is free, however a notification by email to Prof. Veronica Vinciotti is mandatory
- For further information, please contact Prof. Veronica Vinciotti
- Venue: Zoom credentials for the first two weeks: https://unitn.zoom.us/j/81463989506 (Passcode: GMNS2022)
- Language: English