AIDA.3102 Data Modeling and Causal Inference
Id: 042963 Credits: 3-3Description
This course introduces methodological tools through real-world examples to evaluate whether observed relationships in data reflect genuine relationships in the world and, if so, whether those relationships are causal. students will learn how to design informative comparisons to answer substantive questions; how to critically assess arguments that rely on quantitative evidence; which statistical measures are most informative or potentially misleading; how quantitative evidence should---and should not---influence decision-making; and how to make better decisions by integrating data analysis with moral and ethical considerations.
Prerequisites
AIDA.2205 Machine Learning.
Course prerequisites/corequisites are determined by the faculty and approved by the curriculum committees. Students are required to fulfill these requirements prior to enrollment. For courses offered through online or GPS delivery, students are responsible for confirming with the instructor or department that all enrollment requirements have been satisfied before registering.