QR 309: Causal Inference
Cross-listed in economics, quantitative reasoning, and sociology, this course is aimed at upper-level students from a variety of disciplines who have completed previous coursework in statistics.
Course Description:
This course focuses on statistical methods for causal inference, with an emphasis on how to frame a causal (rather than associative) research question and design a study to address that question. What implicit assumptions underlie claims of discrimination? Why do we believe that smoking causes lung cancer? We will cover both randomized experiments – the history of randomization, principles for experimental design, and the non-parametric foundations of randomization-based inference – and methods for drawing causal conclusions from non-randomized studies, such as propensity score matching. Students will develop the expertise necessary to assess the credibility of causal claims and the ability to design and analyze studies that lead to causal inferences. A variety of examples will be used, from economics, psychology, sociology, political science, medicine, and beyond.
Prerequisites: Any one of QR 260, ECON 203, SOC 290, POL 199, POL 299, PSYC 305 or a Psychology 300-level R course; or a Quantitative Analysis Institute Certificate; or permission of the instructor.
Cross-listed as ECON 309, QR 309, and SOC 319.