Causal AI Course

This is a repository for the course CausalAI

The Lectures are on Tuesdays from 8:30 - 11:30 \ The Tutorials are on Friday 08:30 - 10:30.

Lecturer: \ Alexander Quispe Rojas

Teaching Assistant: \

Three Programming Languages:

1. R
2. Python
3. Julia.

Topics this course covers are:

• Prediction/Inference with High Dimensional Linear Models
• Prediction in Modern Nonlinear Regressions (Random Forest and Deep Neural Networks)
• Randomized Control Trials
• Causal DAGs
• Double/debiased Machine Learning
• Heterogeneous Treatment Effects using Causal Trees
• Heterogeneous Treatment Effects using Causal Forest
• Feature Engineering With Deep Learning for Causal and Predictive Inference

Weekly Reports

Every week students have to write a report about a scientific paper. The students will write a report of 1 or 1.5 pages maximum on an assigned article, and will be uploaded the markdown file on github the previous Wednesday of the lecture at 6:00 p.m. The report should address the following points:

• What is the research question of the article?
• What are the strengths and weaknesses of the paper's approach to answering that question?
• How does this document advance knowledge about the question, that is, what is the contribution? (If you can't find any contributions, ask yourself why the editor and referees decided to publish the article.)
• What would be one or two valuable and specific next steps to move forward on this question?

Teamwork

The students will replicate scripts worked in labs and they will work in groups.

X. Website

Video tutorials

1. https://www.youtube.com/watch?v=zyGfECfJ9BY
2. https://www.youtube.com/watch?v=K5xImVmm2Ds

Templates

1. https://bootstrapmade.com/bootstrap-portfolio-templates/
2. https://cssauthor.com/free-bootstrap-portfolio-templates/