A 2-Day Seminar Taught by Felix Elwert, Ph.D.
To see a sample of the course materials, click here.
This course offers an in-depth survey of modern instrumental variables (IV) analysis. IV analysis is an important quasi-experimental technique with numerous applications in economics, the social and biomedical sciences, business, marketing, and education.
IVs allow us to get unbiased estimates of causal effects even when there is selection bias, unobserved confounding, or imperfect compliance. The technique applies equally to randomized trials and observational studies. IV analysis is a very powerful tool—as long as the underlying assumptions are met.
This seminar will take students from the basic Wald estimator up to powerful recent developments, including non-parametric tests of the exclusion assumption. Students will get extensive hands-on experience by analyzing real world examples across the social sciences. We will carefully dissect key technical and substantive assumptions to empower students to recognize, understand, and empirically test these assumptions in practice.
This seminar puts a premium on a practical understanding. We will capitalize on three complementary perspectives: modern potential-outcomes notation, visually intuitive directed acyclic graphs (DAGs), and the traditional algebraic approach. This will enable students to recognize IVs in their own studies, understand assumptions thoroughly, and read the specialist literatures in different fields.
Topics include single instruments, multiple instruments, weak instruments, first-stage diagnostics, over-identification tests, exclusion tests, two-stage least squares (2SLS), natural experiments, encouragement trials, Mendelian randomization, continuous and categorical outcomes, compliance classes, local average treatment effects (LATE), and Balke-Pearl bounds.
We will use the latest commands in Stata and learn theoretical and practical insights that transfer across software packages.
This course is a hands-on course with approximately 2 hours of instructor-led software demonstrations and approximately 2 hours of guided exercises. You should bring a laptop with a recent version of Stata installed (release 13 or later).
WHO SHOULD ATTEND?
If you want to understand how and when you use instrumental variables analysis in practice, this course is for you. The material is equally applicable to experimental and non-experimental data. Participants should have a good working knowledge of multiple regression and basic knowledge of Stata (point-and-click graphical user interface or basic command line operation).
This course is appropriate for researchers (in industry or at universities, faculty, doctoral students, and advanced masters students) with a solid applied background in multiple regression analysis. This course does not require calculus. Students will be given fully executable do-files, so no prior programming experience is necessary.
LOCATION, Format, and MATERIALS
The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at the Courtyard by Marriott Chicago Downtown Magnificent Mile, 165 E Ontario St, Chicago, IL 60611.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and lodging
The fee of $995.00 includes all seminar materials.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Courtyard by Marriott Chicago Downtown Magnificent Mile, 165 E Ontario St, Chicago, IL 60611, where the seminar takes place, at a special rate of $199. In order to make reservations, click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, August 28, 2018.
1. Causal effects
a. ATE: Average treatment effects
b. LATE: Local average treatment effects
c. Identification in randomized trials and observational studies
d. Problems: Confounding, selection, and attrition bias
2. Approaches to IV analysis
a. Potential outcomes
b. Directed acyclic graphs
a. Wald estimator (IV)
b. Two-stage least squares (TSLS)
c. Generalized methods of moments (GMM)
d. Limited information maximum likelihood (LIML)
e. Linear and nonlinear outcome models
f. Standard errors
g. Why one should never control for an IV in OLS
4. Understanding assumptions and their consequences
a. Weak instruments
b. Instrument ignorability
c. Instrument exclusion
5. Testing assumptions
a. Endogeneity tests
b. Over-identification tests
c. Balke-Pearl check
“I am very pleased by this course and the amount I learned in just two days exceeded my expectations. Elwert clearly masters all technical knowledge on this subject, and combines it with sound knowledge on application. Moreover, Elwert is very pedagogical and patiently answers all questions. I would definitely recommend this course to colleagues.”
Tarjei Widding-Havneraas, Haukeland University Hospital
“Highly recommend this interactive, informative course. Felix is an excellent teacher. Beneficial to all researchers, both applied and theoretical.”
Rachel Pownall, Maastricht University
“Incredibly informative. Felix is an amazing teacher with practical advice, rigorous answers to questions, and humor! I highly recommend this course.”
Reza Eladidi, Association pour la Recherche de Thérapeutiques Innovantes en Cancérologie
“This is the second 2-day course I’ve taken with Felix. His ease in bringing under the same statistical roof contributions from so many different fields is unparalleled: economic, classical stats, medical research, and social science, all with his careful translation after what must be considerable intellectual effort. What is unique in these exercises is that he puts himself last and focuses on the topics; you rarely get to see someone of his caliber not citing himself or reminding all that what you just learned and can now impart yourself was his own insight.”
Emil Coman, Health Disparities Institute
“Dr. Elwert is a great instructor: highly knowledgeable, precisely articulate, and encouraging, highly engaging, and enthusiastic in class.”
Hong Chen, Public Health Ontario
“The instructor was very engaging. He was able to answer everyone’s questions, while still covering all the material in the time available. He also made the concepts accessible by explaining everything in a step-by-step fashion.”
Parker Goyer, Stanford University
“This is an excellent course. It is very well organized, comprehensive, and very didactic. The instructor is an expert in the field and very responsive.”
Yossi Shavit, New York University – Shanghai
“The combination of visual representation and equations made the material way more understandable than I have seen in textbooks.”
Thomas Houston, University of Massachusetts, Medical School
“The course goes beyond the technical material and motivates participants to think about causality.”
Eelke De Jong, Radboud University