Causal Discovery

Course Description

Data often provides a projection of the inner workings of real-world systems. Many problems require a deeper understanding of the cause-and-effect relations that underlie the data-generating process. Causal discovery refers to the process of learning a graphical representation of these causal relations, called a causal graph. Such representations can be used for causal effect estimation, as well as other applications, such as system monitoring and diagnosis (e.g., root-cause analysis). In this course, we will learn the foundational principles behind causal discovery. We will explore the fundamental limits of causal discovery under well-defined assumptions and discuss algorithmic approaches for learning causal graphs from data. Prerequisite(s): Students who are currently enrolled in, or have already taken EN.601.678, are not eligible to take EN.601.478.;EN.601.226 AND ((EN.601.475 OR EN.601.675) OR (AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) AND (EN.553.211 OR EN.553.311 OR EN.553.420 OR EN.553.421))

Code

EN.601.478

Credits

3

Subject

Computer Science