36-318 Introduction to Causal Inference
9 units, Carnegie Mellon University
Many social science and scientific inquiries can be framed as causal questions. Does a new cancer treatment cause a reduction in mortality Do financial grants cause students to do better in college Does a new public policy cause an increase in voter turnout When tackling these questions, we frequently come across the phrase correlation does not imply causation. If thats the case, then what does imply causation In this course, we will discuss causal inference methods for measuring causal effects of different interventions e.g., drug treatments, financial grants, and public policies. First, we will discuss how experimentswhere interventions are randomized among subjectscan imply causation when an appropriate experimental design and statistical analysis is used. Then, we will discuss how observational studieswhere interventions are not randomizedcan also imply causation when approaches like propensity score methods, matching, and doubly robust estimation are employed. Finally, we will discuss instrumental variables and regression discontinuity designswhich are frequently used in medicine and public policy for establishing causal inferences. Throughout we will use R to conduct causal analyses. A working knowledge of regression is encouraged, but regression will also be discussed and taught during much of the course.
Prerequisites: 21-325 (min C) or 15-259 (min C) or 36-235 (min C) or 21-425 (min C) or 36-218 (min C) or 36-225 (min C) or 36-219 (min C)
Courseweave needs JavaScript to show sections, workloads and planning tools.