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Instrumental Variables
8 students
Instrumental Variables
Propel Your Research Career with This Online, Intuitive, Self-Paced Course!
Created byDeni Mazrekaj
Last updated 8/2025
English
What you'll learn
- Understand the difference between correlation and causation as well as the underlying theory and assumptions of IV.
- Identify suitable instrumental variables and assess the plausibility of the IV assumptions.
- Estimate local average treatment effects using 2SLS with statistical software and interpret the results.
- Critically evaluate published studies that use IV.
- Design IV studies in real-world research settings.
Explore related topics
Course content
14 sections • 14 lectures • 1h 47m total length
Requirements
- A basic understanding of statistics and linear regression is recommended. Familiarity with statistical software (like Stata or R) is helpful but not required, all necessary tools will be explained.
Description
Stop wading through countless pages filled with confusing equations. This course is structured in such a way that even those with a limited math background can understand the concepts and apply them.
Learning Goals
Upon finishing a course on Instrumental Variables, you will be able to:
Understand the difference between correlation and causation as well as the underlying theory and assumptions of IV.
Identify suitable instrumental variables and assess the plausibility of the IV assumptions.
Estimate local average treatment effects using 2SLS with statistical software and interpret the results.
Critically evaluate published studies that use IV.
Design IV studies in real-world research settings.
Course content
12 modules • 1h 46m total length
Correlation vs Causation
Unmeasured Confounders
Reverse Causality
Randomized Control Trials
Quasi-Experimental Methods
IV – Intuition
Natural Experiments
Two Stage Least Squares
IV – Assumptions
Strengths and Weaknesses of IV
R COMPUTER LAB: 2SLS
Stata COMPUTER LAB: 2SLS
Who is This Course for?
Edooko courses are designed for those who have some grasp of statistics. Our courses are an ideal fit for:
Master students
Ph.D. candidates
Researchers
Policy makers
Data scientists
Anyone looking for training in quantitative methods.
The material covered in our courses is not intended for individuals who are new to statistics and data analysis. If you have some grasp of statistical concepts and are seeking to enhance your understanding of quantitative methods, then our courses are the perfect choice for you. However, if you are at the beginning of statistical training, you may find our courses to be challenging and are advised to start with more foundational coursework first.
Who this course is for:
- This course is ideal for Master's and PhD students, data analysts, policy researchers, and economists interested in learning causal inference techniques and applying instrumental variable methods in real-world research settings.