Instrumental Variables - Online Course
A 3-Day Livestream Seminar Taught by
Felix ElwertThursday, January 28 —
Saturday, January 30, 2027
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar is part of the Causal Inference Certification, a 4-course program designed to strengthen practical skills in causal design, estimation, and interpretation. Contact us to learn how to complete the certification and access discounted pricing.
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.
Starting January 28, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. If you can’t join in real time, recordings will be available within 24 hours and accessible for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
This seminar will take you from the basic Wald estimator up to powerful recent developments, including non-parametric tests of the exclusion assumption. You 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 you 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 you to recognize IVs in your own studies, understand assumptions thoroughly, and read the specialist literature 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, 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 seminar will take you from the basic Wald estimator up to powerful recent developments, including non-parametric tests of the exclusion assumption. You 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 you 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 you to recognize IVs in your own studies, understand assumptions thoroughly, and read the specialist literature 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, 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.
Computing
This is a hands-on course with instructor-led software demonstrations and guided exercises. You should use a computer with a recent version of Stata installed (release 13 or later). You should have basic knowledge of Stata (point-and-click graphical user interface or basic command-line operation).
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
If you are not yet ready to purchase Stata, you may wish to take advantage of StataCorp’s software return policy. Student license purchases may be returned within 7 days of purchase. All other license purchases may be returned within 30 days of purchase.
This is a hands-on course with instructor-led software demonstrations and guided exercises. You should use a computer with a recent version of Stata installed (release 13 or later). You should have basic knowledge of Stata (point-and-click graphical user interface or basic command-line operation).
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
If you are not yet ready to purchase Stata, you may wish to take advantage of StataCorp’s software return policy. Student license purchases may be returned within 7 days of purchase. All other license purchases may be returned within 30 days of purchase.
Who should register?
If you want to understand how and when to use instrumental variables analysis in practice, this course is for you. The material is equally applicable to experimental and non-experimental data. You should have a good working knowledge of multiple regression.
This course is appropriate for researchers (in industry or at universities, faculty, doctoral students, and advanced master’s students) with a solid applied background in multiple regression analysis. This course does not require calculus. You will be given fully executable do-files, so no prior programming experience is necessary.
If you want to understand how and when to use instrumental variables analysis in practice, this course is for you. The material is equally applicable to experimental and non-experimental data. You should have a good working knowledge of multiple regression.
This course is appropriate for researchers (in industry or at universities, faculty, doctoral students, and advanced master’s students) with a solid applied background in multiple regression analysis. This course does not require calculus. You will be given fully executable do-files, so no prior programming experience is necessary.
Seminar outline
Day 1
-
- Preliminaries: linear models
- Graphs and path algebra
- IV: problem and solution
- Real example: Angrist’s draft lottery
- Instrumental variables in Stata
Day 2
-
- Understanding exclusion violations
- Real example: Angrist & Krueger’s quarter of birth IV
- Important little-known facts
- How to find good instruments
- Estimation of overidentified models: 2SLS, GMM, LIML
- IV vs. 2SLS
Day 3
-
- Finding and creating multiple IVs
- GMM: getting standard errors right
- Testing and correcting IV assumptions
- IV for measurement error
- Effect heterogeneity and LATE
Day 1
-
- Preliminaries: linear models
- Graphs and path algebra
- IV: problem and solution
- Real example: Angrist’s draft lottery
- Instrumental variables in Stata
Day 2
-
- Understanding exclusion violations
- Real example: Angrist & Krueger’s quarter of birth IV
- Important little-known facts
- How to find good instruments
- Estimation of overidentified models: 2SLS, GMM, LIML
- IV vs. 2SLS
Day 3
-
- Finding and creating multiple IVs
- GMM: getting standard errors right
- Testing and correcting IV assumptions
- IV for measurement error
- Effect heterogeneity and LATE
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Need funding from your employer or institution? Use our funding request template.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Need funding from your employer or institution? Use our funding request template.
Our Tax ID number is 26-4576270.