Causality is a fundamental concept, which plays critical role in several areas today including machine learning and data science. In this course, we are going to learn the tools for modeling probabilistic causality. We will learn causal graphs, and how they can be used for estimating interventional queries. We will also cover learning causal graphs from observational as well as interventional data. Finally, we are going to briefly discuss the notion of counterfactuals. The course also has a research component and students are expected to develop or outline a novel algorithmic idea/solution or a new application of the tools they learn in the class to their research, which they will describe in their project presentation and project report.
You can find the tentative syllabus here.
This is a research-oriented course. Therefore, most of the grade will be based on a research presentation and a 3-page research report to be submitted at the end of the semester. Students are expected to work on the project on their own (i.e., group size of 1) with guidance from the instructor. Students are encouraged to start the project as early as possible in the semester and frequently consult with the instructor in office hours or on Piazza. There is also one midterm and one paper presentation, each worth 25 percent of your grade.
Date : 9/21 Tuesday, in-lecture.
Topics : Basics of d-separation, do calculus and identifiability on causal graphs.
Date : Project presentations start 11/2 (tentative). Exact date for each student will be determined later in
the semester based on the enrollment.
Format : Presentation of a research paper. Instructor will provide a pool of papers to choose from. Other papers are acceptable with the instructor’s approval.
Date : Project presentations start 11/23 (tentative). Exact presentation date for each student will be determined later in the semester based on the enrollment.
Format : A 30-minute presentation outlining the research contribution to be submitted as a project report.
This will be an important feedback from the instructor and the other students which should be taken
into account in the project report. Please start the project as early as possible in the semester and discuss with the instructor throughout the semester to get OK for the scope in office hours, via email or over Piazza.
Date : 12/15
Format : A 3-page pdf – excluding appendix and references – to be submitted to the instructor via email with the subject line ECE695 Project Report. The paper should contain novelty either as a new algorithmic
solution to a problem within the scope of the course or a novel promising research direction with the detailed outline of the approach.