This course focuses on teaching causal fairness analysis through a Translation Tutorial. The learning outcomes include understanding Structural Causal Models (SCM), the Causal Fairness Framework, and various counterfactual effects. Students will learn to apply these concepts to analyze and address discrimination in datasets. The course employs a tutorial format and is intended for individuals interested in the intersection of causal inference and fairness in machine learning.
Translation Tutorial - Causal Fairness Analysis
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Structural Causal Model (SCM)
SCM M + Causal Diagram G
Berkeley admission Students apply for university's admission (7), and choose specific departments to which they wish to
COMPAS prediction. Northpointe are trying to predict represents the age, variable represents prior convictions, and
UCI Adult). The US census data records whether a person
Causal Fairness Framework: Step 2
Causal Fairness Framework (Summary)
The "Standard Fairness Model"
Discrimination in UCI Adult
Counterfactual Direct Effect
Counterfactual Indirect Effect
Thought Experiment III
Counterfactual Spurious Effect
Causal Explanation Formula
Taught by
ACM FAccT Conference
