CSCI 379
Causal Inference Spring 2024
Division III Quantitative/Formal Reasoning
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Does X cause Y? If so, how? And what is the strength of this causal relation? Seeking answers to such causal (as opposed to associational) questions is a fundamental human endeavor; the answers we find can be used to support decision-making in various settings such as healthcare and public policy. But how does one tease apart causation from association–early in our statistical education we are taught that “correlation does not imply causation.” In this course, we will re-examine this phrase and learn how to reason with confidence about the validity of causal conclusions drawn from messy real-world data. We will cover core topics in causal inference including causal graphical models, unsupervised learning of the structure of these models, expression of causal quantities as functions of observed data, and robust/efficient estimation of these quantities using statistical and machine learning methods. Concepts in the course will be contextualized via regular case studies.
The Class: Format: lecture
Limit: 24
Expected: 24
Class#: 3243
Grading: no pass/fail option, no fifth course option
Requirements/Evaluation: Problem sets, programming exercises, empirical analyses, case studies, and a final project.
Prerequisites: CSCI 136, and either CSCI 256 or STAT 201/202.
Enrollment Preferences: Computer science majors and prospective majors.
Distributions: Division III Quantitative/Formal Reasoning
QFR Notes: This course heavily relies on discrete mathematics, algorithms, and elementary statistics. There will be regular assignments requiring rigorous quantitative or formal reasoning.
Attributes: COGS Interdepartmental Electives

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