What This Document Is
This document is a comprehensive course syllabus for STAT C239A / Political Science C236A: The Statistics of Causal Inference in the Social Sciences, offered at the University of California, Berkeley. It outlines the course structure, expectations, and core areas of study for students seeking advanced understanding of causal inference methodologies. It serves as a foundational guide for navigating the complexities of determining cause-and-effect relationships within social science research.
Why This Document Matters
This syllabus is essential for prospective students, current students enrolled in the course, and anyone interested in the field of causal inference. It’s particularly valuable for those with a background in statistics, political science, economics, sociology, public health, or related disciplines. Understanding the course’s scope and requirements *before* committing to the full material will help you assess its relevance to your academic or professional goals. It’s also a useful reference for researchers looking to understand the curriculum typically employed in rigorous causal inference training.
Topics Covered
* Potential Outcomes Framework
* Randomized Experiments (including noncompliance scenarios)
* Observational Studies (with and without ignorable treatment assignment)
* Instrumental Variables
* Regression Discontinuity Designs
* Sensitivity Analysis
* Permutation Inference
* Applications across various social science fields
* Statistical modeling and regression techniques
* Programming for statistical analysis
What This Document Provides
* Detailed course logistics: including meeting times, location, and instructor contact information.
* A clear outline of prerequisites and recommended background knowledge.
* A breakdown of the grading components and their respective weights.
* Information regarding course policies on collaboration, academic integrity, and incomplete grades.
* A schedule indicating key dates, such as the midterm examination.
* A list of recommended (though not required) resources, including textbooks and software.
* Guidance on utilizing the R programming language for data analysis.