Computer Science 341CD - Topics: 'Casual Inference for Data Science'

You might have heard the phrase "correlation is not causation" - but then, what is causation? For example, how did scientists determine that smoking causes lung cancer? This course will explore how to ask and answer causal questions using data. We will learn the fundamentals of estimating cause-and-effect relationships, drawing from foundations in computer science, statistics, and economics. We will also use modern data science tools coding in Python to run simulations, work with data, and communicate our findings. Students will get hands-on experience thinking through causal study design and analyzing data across real-world applications in healthcare, public policy, education, and more. This course will have a substantial mathematical component, building off probability concepts seen in MATH-232.

Prereq: COMSC-205 and MATH-232.