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 14, SE or MP will do).
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 seminar meets Friday, October 27 and Saturday, October 28 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. The class will meet from 9 to 5 each day with a 1-hour lunch break.
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 includes all course materials. The early registration fee of $895 is available until September 27.
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 Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $154. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STAT26 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, September 26.
If you make reservations after the cut-off date ask for the Statistical Horizon’s room rate (do not use the code) and they will try to accommodate your request.
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
“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
“Excellent! Every word mattered!”
“I enjoyed most the link between theory and real applications.”
Annalisa Ferrando, European Central Bank
“Great introduction to a theme that is quite complex. I particularly appreciated that Felix drew on examples from several different disciplines. Thank you!”