Courses on Causal Modelling

Author: Sergio Focardi, PhD

 

Causal modelling is a new machine learning technology. It is very useful as a decision support tool because it allows to understand quantitatively the causal effects of decisions. In the last three decades scientists and philosophers have developed algorithms for learning causal structures from data. Managers and investors can now acquire a quantitative view, based on data, of how their decisions will impact their business.

 

The optimal deployment of causal discovery algorithms requires an understanding of the working of causation and of causal models. In particular, it requires an understanding of the role of data. Creating causal models is an iterative process were data and models are intertwined.

 

The course on Frontdoor Adjustment offers an in-depth discussion on the methodologies to estimate causal effects in the presence of hidden, non-observed, confounders.

 

The course on Dynamic Causal Models offers an in-depth discussion of the methodologies of causal models applied to time series and stochastic processes

 

 

​Click here for the brochure of the course Frontdoor Adjustment for Causal Models​​

 

Click here for the brochure on the course on Dynamic Causal Models