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Econometric Methods for Causal Inference
EPI 268 Fall 2017 (2 or 3 units)


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:

  • Identify opportunities for rigorous evaluation of natural experiements;
  • Enumerate the (oft unstated) assumptions involved in interpreting research that aims to draw causal inferences; and
  • Implementation the research techniques covered in class.

BIOSTAT 200, BIOSTAT 208, EPI 203, or equivalent experience. Experience with Stata.


Course Director:
Phone: 415-476-8045
email: justin.white@ucsf.edu


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.

  1. Weekly Quizzes
    Quizzes will be completed online, typically due seven days after each class. Late quizzes will not be graded except in extraordinary circumstances. The main purpose of the quizzes is to ensure that you have completed and understood the content of the assigned readings and in-class lecture for that week. You may not work on the quizzes with others.
  2. Problem Sets
    There are two problem sets, each due at the beginning of class on the due date. Late assignments will not be graded except in extraordinary circumstances. Assignments may be handwritten or typed, but must be legible. You can work with others, but each student must turn in their own assignment, and must acknowledge intellectual contributions of anyone who has assisted them.
  3. Original Empirical Paper (3-unit option only)
    The paper will be based on a topic of your choice, using data of your choice, and should apply a technique covered in this class. You are encouraged to discuss your paper with others, but as always you must acknowledge any intellectual contributions made by others. See the Box course folder for more detailed guidelines. Extra class sessions will be offered to help students in the 3- unit option to develop an empirical paper. A one-page proposal for the paper is due by email on October 27. The paper is due by email on December 14.

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:


Weekly quizzes 6 quizzes 50% 20%
Problem sets 2 problem sets 50% 30%
Empirical paper 1 paper

UCSF Graduate Division Policy on Disabilities


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.

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