Foundations of Causal Graphical Models and Structure Discovery Slides

Foundations of Causal Graphical Models and Structure Discovery

Understanding causality is arguably the ultimate goal in any field of science. Knowledge about causality allows one to predict a system’s behavior under external interventions, a key step towards understanding and engineering that system. While the gold standard for establishing causality remains controlled experimentation, such experimentation is not always possible due to practical or ethical concerns. Inferring causality from observational data thus has become an increasingly popular area of study, attracting researchers from statistics, philosophy, machine learning, artificial intelligence, and data science. The ever-changing field of causal discovery makes for a steep learning curve for students and junior researchers. This conference aims to provide a deep review of causal discovery to help orient researchers new to the topic.

List of lectures:

1. Introduction – A Big Picture of Causality

2.  Preliminaries_Probability_GraphicalModels

3.  Guest Lecturer Peter Spirtes: Modern History of Causal Inference

4.  Identification_Causal_Effects_Counterfactual

5. Multivaraite_analysis_and_Constraint_Score_Causal_Discovery

6.  Guest Lecturer Peter Spirtes: Unmeasured Confounders, Selection Bias, Missing Values

7.  Linear_nonGaussian_methods_Confounders_Cycles

8.  Practical_Issues_in_Causal_Discovery

9. Causal_Representation_Learning

10. Causality_MachineLearning