Econometric Methods for Causal Inference
Epidemiologists and clinical researchers are increasingly seeking to estimate the causal effects of health-related policies, programs, and interventions. Economists have long had similar interests, and have developed and refined methods to estimate causal relationships. Examples include difference-in-differences, instrumental variables, and regression discontinuity. This course introduces a set of econometric tools and research designs in the context of health-related questions. The course topics are especially useful for evaluating natural experiments — situations in which comparable groups of people are exposed or not exposed to conditions determined by “nature” (not by a researcher), as occurs with a government policy or a disease outbreak.
At the end of this course, scholars will be able to:
BIOSTAT 200, BIOSTAT 208, EPI 203, or equivalent experience. Experience with Stata.
Each week, new material is introduced via lecture and readings. Lectures will consist of discussions of course concepts, critiques of papers applying a research technique covered in class, and implementation of research techniques in Stata. These will take place on Fridays from 1:30 PM to 4:00 PM, Sept. 29 to Dec. 8. Students’ knowledge is assessed outside of class through weekly quizzes or problem sets.
All course materials and handouts will be posted on the course's online syllabus.
Mastering 'Metrics: The Path From Cause to Effect by J. Angrist and J Pische. Princeton University Press. 2015.
Stata Statistical Software (Stata Corporation, College Station, TX) will be used; version 13 or higher is acceptable. A six-month student license for Stata/IC is the least expensive option that will be suitable to complete all course assignments, but Stata/SE is recommended for robust future use. The TICR Program has arranged for a sizeable discount for UCSF-affiliated personnel.
Books may be purchased either through the publisher or a variety of commercial venues (e.g., Amazon.com).
Students may find other textbooks useful to enhance their learning. Textbooks which discuss the material at a slightly less advanced level than our course include:
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by G. W. Imbens and D. B. Rubin. Cambridge University Press. 2015. (The most thorough and current statement of potential outcomes models for causal inference.)
Mostly Harmless Econometrics by J. Angrist and J-S Pischke. Princeton University Press. 2009. (The more mathematically advanced companion to Mastering ‘Metrics.)
Introductory Econometrics: A Modern Approach by J. M. Wooldridge. Cengage Learning. 6th edition. 2016. (A thorough, introductory treatment of a broad range of econometric applications.)
A Guide to Econometrics by P. Kennedy. Wiley-Blackwell. 6th edition. 2008. (Intuitive feel for econometric concepts, alongside more technical discussion.)
Microeconometrics Using Stata by A. C. Cameron and P. Trivedi. Stata Press. 2009. (A useful, though aging, primer on using Stata for econometrics; the book also has parallel content to their econometrics textbook.)
Health Econometrics Using Stata Partha Deb, Edward C. Norton, Willard G. Manning. Stata Press. 2017. (Overview of a variety of econometric modeling approaches in Stata, including many not covered in this course, e.g., GLM, count models, and a mass at zero.)
Grading will be based on quizzes, problem sets and a paper:
To apply for this course, please fill out and submit the application below. Course fees are covered by the Department of Epidemiology and Biostatistics. The deadline for application is September 8, 2017. Only one application needs to be completed for all courses desired during the quarter.